{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lession 5a: Plotting with Pandas\n",
"\n",
"The Python visualization ecosystem is vast and can come across as daunting at first. In fact, [PyViz](https://pyviz.org/index.html) is a website that was created for the sole purpose of helping users decide on the best open-source Python data visualization tools for their needs. I highly recommend you take some time to explore the resource.\n",
"\n",
"However, since we have spent the last several modules getting comfortable using Pandas for our statistical computing needs, it is only fitting to continue using Pandas and explore the high-level plotting capabilities it provides before exploring other libraries. Consequently, this lesson is mainly focused on making you capable of creating basic plots directly with your Pandas Series and DataFrame objects."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning objectives\n",
"\n",
"By the end of this lesson you will be able to:\n",
"\n",
"* Create univariate plots based on Series\n",
"* Create bivariate plots based on DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"\n",
"Load Pandas to provide you access to the join functions we’ll cover in this lesson."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This lesson will also use the Complete Journey data; but before we start we’ll join the transactions data with the products and household demographics data:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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\n",
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" \n",
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\n",
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\n",
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household_id
\n",
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store_id
\n",
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basket_id
\n",
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product_id
\n",
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quantity
\n",
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sales_value
\n",
"
retail_disc
\n",
"
coupon_disc
\n",
"
coupon_match_disc
\n",
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week
\n",
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transaction_timestamp
\n",
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manufacturer_id
\n",
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department
\n",
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brand
\n",
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product_category
\n",
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product_type
\n",
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package_size
\n",
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age
\n",
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income
\n",
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home_ownership
\n",
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marital_status
\n",
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household_size
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household_comp
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kids_count
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2017-01-01 11:53:26
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ROLLS: BAGELS
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2017-01-23 12:50:57
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2017-01-29 12:25:17
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2017-02-13 10:56:45
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"text/plain": [
" household_id store_id ... household_comp kids_count\n",
"0 900 330 ... 2 Adults No Kids 0\n",
"1 900 330 ... 2 Adults No Kids 0\n",
"2 900 330 ... 2 Adults No Kids 0\n",
"3 900 330 ... 2 Adults No Kids 0\n",
"4 900 330 ... 2 Adults No Kids 0\n",
"\n",
"[5 rows x 24 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from completejourney_py import get_data\n",
"\n",
"cj_data = get_data()\n",
"df = (\n",
" cj_data['transactions']\n",
" .merge(cj_data['products'], how='inner', on='product_id')\n",
" .merge(cj_data['demographics'], how='inner', on='household_id')\n",
")\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `.plot` attribute\n",
"\n",
"Both Pandas Series and DataFrames have a `.plot` attribute that allow us to plot our data. This `.plot` attribute is a little unique as you can call it as a method and specify the plot of interest using an argument:\n",
"\n",
"```python\n",
"df.plot(kind='scatter', ...)\n",
"```\n",
"\n",
"Or you can use it to access sub-methods:\n",
"\n",
"```python\n",
"df.plot.scatter(...)\n",
"```\n",
"\n",
"```{note}\n",
"In this lesson and throughoug most of this class you will notice that I prefer to use `.plot` to access sub-methods. However, this is simply a preference as you can accomplish the same output with either approach.\n",
"```\n",
"\n",
"`.plot` is pretty versatile as it (and all the sub-methods) provide a lot of parameters to control and refine our plots. Currently, the available plots that we can make with `.plot` include:\n",
"\n",
"* __line__ : line plot (default)\n",
"* __bar__ : vertical bar plot\n",
"* __barh__ : horizontal bar plot\n",
"* __hist__ : histogram\n",
"* __box__ : boxplot\n",
"* __kde__ : Kernel Density Estimation plot\n",
"* __density__ : same as ‘kde’\n",
"* __area__ : area plot\n",
"* __pie__ : pie plot\n",
"* __scatter__ : scatter plot (DataFrame only)\n",
"* __hexbin__ : hexbin plot (DataFrame only)\n",
"\n",
"### Knowledge check\n",
"\n",
"```{admonition} Questions:\n",
":class: attention\n",
"Check out the `.plot` documentation for [Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html) and [DataFrames](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html).\n",
"\n",
"1. What parameter would you use to control the figure size?\n",
"2. What parameter would you use to add a title?\n",
"3. What parameter(s) would you use to log scale an x and/or y axis?\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting a Series\n",
"\n",
"Often, our analysis starts by understanding individual variables within our data and analyzing univariate distributions helps with this process. Consequently, its very common to simply select a single Series and visualize the distribution of values in that series. \n",
"\n",
"Let's start by creating a histogram of the transaction `sales_value`s in our Complete Journey data. We can do this with `hist()`:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df['sales_value'].plot.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You probably notice a couple of things above:\n",
"\n",
"1. What's with the `` output above our plot?\n",
"2. We have many zeroes in our data plus this feature is heavily skewed. Consequently, our histogram is not very informative at this point.\n",
"\n",
"When we make a plot, the output will include the name of the object, which is what we see happening with ``. \n",
"\n",
"```{tip}\n",
" We can remove this output by adding a `;` after the plotting function call.\n",
"```\n",
"\n",
"As for #2, we can make some adjustments such as remove any zero dollar transactions, log transform our axis, and increase the number of bins. This helps to pull out additional insights in our `sales_value` distribution. For example, we see we have a lot of very low dollar transactions and the frequency decreases as the transaction dollar amount increases. However, we also see an increase in transactions right at the \\$200 mark but that decreases quickly. There are also a few outlier transaction values that are around the \\$600 and \\$800 value marks."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(\n",
" df.loc[df['sales_value'] > 0, 'sales_value']\n",
" .plot.hist(log=True, bins=30, title='Distribution of Sales Values')\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Box plots and kernel density estimation (KDE) plots are an alternative way to view univariate distribtions. For example, let's compute the total `sales_value` across all stores. The resulting `sales_by_store` object is a Series. A boxplot provides a lot of information (read about them [here](https://www.simplypsychology.org/boxplots.html#:~:text=In%20descriptive%20statistics%2C%20a%20box,(or%20percentiles)%20and%20averages.)). We can see that the median (red line) is around $10^2 = 100$ and the interquartile range is between somewhere around 50-15000. We also see we have some outliers on the upper end."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sales_by_store = df.groupby('store_id')['sales_value'].sum()\n",
"\n",
"# boxplot\n",
"sales_by_store.plot.box(logy=True, title='Distribution of total sales across all stores');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can quickly compare our boxplot with our numeric distribution and we see our they are similar (median: 157, interquartile range: 27-16000)."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 280.000000\n",
"mean 9374.588536\n",
"std 15141.356992\n",
"min 1.000000\n",
"25% 27.265000\n",
"50% 156.740000\n",
"75% 16167.215000\n",
"max 99608.720000\n",
"Name: sales_value, dtype: float64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_by_store.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The KDE plot (which is also produced with `.plot.density()`) provides a smoothed histogram."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sales_by_store.plot.kde(title='Distribution of total sales across all stores');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `.plot` sub-methods work exceptionally well with time series data. To illustrate, let's create a Series that contains the `sales_value` of each transaction with the `transaction_timestamp` as the index."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"transaction_timestamp\n",
"2017-01-01 11:53:26 0.50\n",
"2017-01-23 12:50:57 0.25\n",
"2017-01-29 12:25:17 0.25\n",
"2017-02-13 10:56:45 0.50\n",
"2017-02-19 12:11:58 0.50\n",
"Name: sales_value, dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales = df.set_index('transaction_timestamp')['sales_value']\n",
"sales.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A handy method we have not talked about is [`resample()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.resample.html) which allows us to easily convert time series data. For example, if we wanted to sum all `sales_value`s by the hour we can use `.resample('H')` followed by `.sum()`."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"transaction_timestamp\n",
"2017-01-01 11:00:00 0.50\n",
"2017-01-01 12:00:00 13.69\n",
"2017-01-01 13:00:00 20.43\n",
"2017-01-01 14:00:00 36.58\n",
"2017-01-01 15:00:00 553.79\n",
" ... \n",
"2018-01-01 00:00:00 337.78\n",
"2018-01-01 01:00:00 221.50\n",
"2018-01-01 02:00:00 142.84\n",
"2018-01-01 03:00:00 63.56\n",
"2018-01-01 04:00:00 10.52\n",
"Freq: H, Name: sales_value, Length: 8754, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales.resample('H').sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we followed this sequence of code with `plot.line()` we get a line plot of the total sales values on the y-axis and the date-time on the x-axis."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(\n",
" sales\n",
" .resample('H')\n",
" .sum()\n",
" .plot.line(figsize=(10,4))\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above plot is a bit busy since we're plotting values for every hour over the course of a year. Let's reduce the frequency and, instead, sum the `sales_value`s by day (`.resample('D')`). Now we see a bit more of a descriptive pattern. It looks like there is routinely higher sales transactions on particular days (probably certain days of the week such as weekends)."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(\n",
" sales\n",
" .resample('D')\n",
" .sum()\n",
" .plot.line(figsize=(10,4))\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's validate our assumption above regarding the weekly shopping pattern. The below code chunk performs the same as above where we compute total daily sales across all days of the year but then we follow that up by extracting the name of the weekday from the date-timestamp and then grouping by the day of week and computing the median and interquartile range (IQR) for all daily sales for the year.\n",
"\n",
"```{note}\n",
"If you have not yet seen code that looks like `lambda idx: idx.day_name()` do not worry. These are called lambda (anonymous) functions and we'll discuss them more in module 6.\n",
"```\n",
"\n",
"We definitely see that Saturday and Sunday are the weekdays with the heaviest sales value transactions."
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Tl5e3VzpKKaWUOnDGGLZUbGkOvgqXUlJbAkBCWAJZyc3BV3rfrtXZr8cGY797L4c1O3b7NM2MlL789pzMdvfJz89nwIDmcW/T0tJalHq19uyzz3LGGWd4nosIU6ZMQUT46U9/yvXXX3/4GVdKKaV6GGMMm8o3saRwCUsLbPBVVmfHjE8MT+To/kczMWkiWclZDIwa2KWCr9Z6bDDmL22NV7uvG2Du3Lk8++yzfPPNN5518+fPJyUlhaKiIk477TRGjRrFiSee2GH5VUoppboDt3GzsXwjSwqWsKxwGUsLlrJrzy4AkiOSOS7lOE/JV1pUWpcOvlrrscHY/kqwOkpaWhrbtzfPlZ6Xl0dKSspe+61cuZIZM2bw0UcfERfXPC96076JiYmcd955LF68WIMxpZRSvY7buNmwa4On2nFZ4TLK95QDkBKRwglpJ3iCr9TI1G4VfLXWY4Mxf5k4cSIbNmxgy5YtpKamMnPmTF577bUW+2zbto3zzz+fl19+mREjRnjWV1dX43a7iYqKorq6mk8++YTf/OY3nf0SlFJKqU7ncrv4ftf3LYKv3fW2uVFqZConDTiJrKQsspJt8NWTaDDmY0FBQTz22GNMnToVl8vFNddcQ2ZmJk8++SQAN9xwA7///e8pLS3lxhtv9ByzdOlSCgsLOe+88wBobGzk0ksv5fTTT/fba1FKKaU6isvtYt2udba9V4ENviobKgEYEDWAUwedaoOvpCz6R/b3c247lhzinNx+l5WVZVqPwbV27VpGjx7tpxx1nt7yOpVSSvUcje5G1pWt85R8fVf4HVUNVQAM6jvIU+qVlZRFckSyn3PreyKyzBiT1dY2LRlTSimllM81uBtYW7rWE3wtL1pOdUM1AOl90zlj8BmeACwxPNHPufUvDcaUUkopddga3A3klOR4Blj9rug7ahtrARgSPYSzh5ztCb7iw3Rgc28ajCmllFLqoDW4GlhdutqWfBUsJbs42xN8DYsZxrlDzyUrOYujko7S4Gs/NBhTSiml1H7Vu+pZVbLKU+24omgFda46AIb3G855w87zBF+xobF+zm33osGYUkoppfayx7WHlcUrPaPbryhewR7XHgRhRL8RXDjiQrKSsjgy6Uj6hfbzd3a7NQ3GlFJKKUVdYx0ri1d6phdaWbySenc9gjAqdhQ/GvkjspJsyVd0n2h/Z7dH0WCsA3z88cfcdtttuFwuZsyYwd13391i+7x58zj33HMZPNjOEn/++efr4K5KKaU6VW1jLSuKV3jafK0qWUWDu4EACWBU7CguGXUJWclZTEicoMFXB9NgzMdcLhc33XQTn376KWlpaUycOJFp06aRkZHRYr8TTjiB999/30+5VEop1dvUNNSQXZztqXZcVbKKRncjARJARmwGl4++3BN8RYVE+Tu7vYoGYz62ePFihg0bxpAhQwC4+OKLmTVr1l7BmFJKKdWRahpqWF603NPgPqckh0bTSKAEkhmXyZUZV5KVZIOvyJBIf2e3V+u5wdhHd0PBKt+mmTwWzniw3V3y8/MZMGCA53laWhqLFi3aa78FCxYwbtw4UlJSeOihh8jM9M/E5koppXqGqvoqG3wVLmFZwTJySnNwGRdBEkRmfCZXj7marKQsxieOJyI4wt/ZVV56bjDmJ21NL9V6JvkjjzySrVu3EhkZyYcffsj06dPZsGFDZ2VRKaVUD1BZX9lc8lWwlDVla3AbN0EBQYyNH8s1Y64hKzmL8QnjCQ8O93d2VTt6bjC2nxKsjpKWlsb27ds9z/Py8khJSWmxT9++fT3LZ555JjfeeCMlJSXEx+ugeEoppfZt++7tvLH+DZYULmFd2Trcxk1wQDBj48dy3djryErOYlzCOMKCwvydVXUQem4w5icTJ05kw4YNbNmyhdTUVGbOnMlrr73WYp+CggKSkpIQERYvXozb7SYuLs5POVZKKdUdFFYXcvWcq9lVt4txCeO4/ojrmZg0kSMSjiA0KNTf2VOHQYMxHwsKCuKxxx5j6tSpuFwurrnmGjIzM3nyyScBuOGGG3jrrbf497//TVBQEGFhYcycOXOvqkyllFKqSU1DDbd8cQtV9VW8ftbrjIwd6e8sKR+Stto4dQdZWVlm6dKlLdatXbuW0aNH+ylHnae3vE6llFLgcru4fe7tfJX/FY/+8FFOTDvR31lSh0BElhljstraFtDZmVFKKaXUgXto6UPMy5vHPZPu0UDM14rWwqybIfcbv2ZDqymVUkqpLurVta/yytpXuCLjCi4edbG/s9MzGAMbP4eFj8OmLyAoDFImQPrxfsuSBmNKKaVUF/Tl9i/565K/cvKAk/nFUb/wd3a6v4ZaWPkGLPw3FK+DyGT44a8h6xoIj/Vr1jQYU0oppbqYtaVrufOrOxkVO4oHT3iQwIBAf2ep+6oshCXPwNJnoaYUko+A8/4DmedDUIi/cwdoMKaUUkp1KQXVBdz8+c1E94nmsR8+pgO2HqqC1bDwCVj1X3A1wMgzYPKNtjqyi41goMGYUkop1UVUN1Rzyxe3UN1YzYunv0hCeIK/s9S9uN2w8VNY8Bhs+QqCw+HIq2Dy/0HcUH/nbp80GOsAH3/8Mbfddhsul4sZM2Zw9913t9j+t7/9jVdffRWAxsZG1q5dS3FxMbGxsaSnpxMVFUVgYCBBQUG0Hr5DKaVUz9TobuTOL+9kw64NPH7K4zqW2MGor4EVr9v2YKUbICoFTr0fjroawvr5O3f7pcGYj7lcLm666SY+/fRT0tLSmDhxItOmTSMjI8Ozz5133smdd94JwHvvvcfDDz9MbGxz48G5c+fq1EhKKdWLGGP4y+K/8HX+1/x68q85LvU4f2epe9i9ExY/Bcueh9pdtlfkBc9CxrkQGOzv3B0wDcZ8bPHixQwbNowhQ4YAcPHFFzNr1qwWwZi3119/nUsuuaQzs6iUUqqLeXXtq8xcP5OrM6/mRyN/5O/sdH07sm17sNVvg7sRRp8Nk2+CgZO7XHuwA9Fjg7G/LP4L68rW+TTNUbGjuGvSXe3uk5+fz4ABAzzP09LSWLRoUZv71tTU8PHHH/PYY4951okIU6ZMQUT46U9/yvXXX++bzCullOqS5m6by1+X/JVTBp7Cz476mb+z03W5XfD9x7DgCdj6DYREwsQZcPRPIXawv3N3WHpsMOYvbU0vta95J9977z2OO+64FlWU8+fPJyUlhaKiIk477TRGjRrFiSfqiMtKKdUT5ZTmcNfXd5EZl8mfT/gzAaIT4+xlTxVkvwaL/g1lmyF6AEz5Axx5JYRG+zt3PnFYwZiI3AZcBwjwtDHmnyISC7wBpAO5wI+MMbuc/e8BrgVcwK3GmDnO+qOAF4Aw4EPgNnOYk2burwSro6SlpbF9+3bP87y8PFJSUtrcd+bMmXtVUTbtm5iYyHnnncfixYs1GFNKqR6ooLqAWz6/hZg+MTx6yqOEBYX5O0tdS0We0x7sBairgLSJdpDW0dMgsGeVJR1yCC4iY7CB2CRgHHC2iAwH7gY+N8YMBz53niMiGcDFQCZwOvCEiDSNYvdv4HpguPM4/VDz5W8TJ05kw4YNbNmyhfr6embOnMm0adP22q+iooIvv/ySc88917OuurqayspKz/Inn3zCmDFjOi3vSimlOkdVfRU3fn4jtY21PH7K48SHaactj/xl8Na18M8j4NtHYcjJcO2nMOMzGHN+jwvE4PBKxkYDC40xNQAi8iVwHnAucJKzz4vAPOAuZ/1MY8weYIuIbAQmiUgu0NcYs8BJ5yVgOvDRYeTNb4KCgnjssceYOnUqLpeLa665hszMTJ588kkAbrjhBgDeeecdpkyZQkREhOfYwsJCzjvvPMAOeXHppZdy+undNi5VSinVhkZ3I3d8dQebyzfzxKlPMLzfcH9nyf/cLlj3ASx4HLYvhD597dhgk66HfoP8nbsOdzjB2GrgjyISB9QCZwJLgSRjzE4AY8xOEUl09k8FFnodn+esa3CWW6/fi4hcjy1BY+DAgYeR9Y515plncuaZZ7ZY1xSENbn66qu5+uqrW6wbMmQIK1as6OjsKaWU8hNjDA8ufpD5+fP57TG/5diUY/2dJf+q2w3LX4FFT0L5VogZCFP/DBMuh9C+/s5dpznkYMwYs1ZE/gJ8ClQBK4DGdg5pqxW7aWd9W+d8CngKICsr67DalCmllFKd7eU1L/PG+jf4yZifcOGIC/2dHf8p3waL/gPfvQR7dsOAyTDlARh1NvTCeTgPq+LVGPMs8CyAiPwJW6pVKCL9nVKx/kCRs3seMMDr8DRgh7M+rY31SimlVI/x+bbPeWjpQ5w26DRuP/J2f2fHP7YvtlWRa2cDApnT7fhgaUf5O2d+dbi9KRONMUUiMhA4HzgGGAxcBTzo/J3l7D4beE1E/gGkYBvqLzbGuESkUkQmA4uAK4FHDzVPxph9DiXRExxmJ1OllFJ+kFOSw91f3c3Y+LH86fg/9a4hLFyNsO49G4TlLYE+0XDMzXZ8sOi0/R/fCxxul4T/OW3GGoCbjDG7RORB4E0RuRbYBlwEYIzJEZE3gTXY6sybjDEuJ53/o3loi484xMb7oaGhlJaWEhcX1yMDMmMMpaWlhIaG+jsrSimlDtCOqh3c/MXNxIXF8cgPHyE0qJd8htdV2GrIRU9BxTboNxjO+BuMvxT6RPo7d12KdNeSlqysLNN6Eu2Ghgby8vKoq6vzU646XmhoKGlpaQQHd585t5RSqreqrK/kyo+upLC6kJfPfJmhMUP9naWOV7bFtgdb/jLUV8Gg4+GYG2HE6b2yPVgTEVlmjMlqa1uPGqwjODiYwYO795QISimleoYGdwN3fHkHuRW5PHHqEz07EDMGti2EhY/bISokAMZcAJNvhJTx/s5dl9ejgjGllFKqKzDG8OdFf+bbHd/yu2N/xzEpx/g7Sx3D1QBrZtn2YDu+g9AYOO52mHQd9G179hm1Nw3GlFJKKR97MedF/vv9f5kxdgbnDz/f39nxvdpdsOxFO13R7nyIGwZn/R3GXQIhEfs/XrWgwZhSSinlQ59t/Yx/LPsHU9OncsuEW/ydHd8q3WQHaF3+KjRUw+AT4ax/wPApENCLeoj6mAZjSimllI+sKl7FPV/fw9iEsfzhuD/0jCEsjIGt821V5PqPICAIxl5kG+Unj/V37noEDcaUUkopH8ivyvcMYfGvk//V/YewaKyHnHdgwWNQsBLCYuHEO2HiDIhK8nfuehQNxpRSSqnDtLt+Nzd9dhMN7gaeP+V54sLi/J2lQ1dTBkufg8VPQ1UBxI+Ecx6BI34MwWH+zl2PpMGYUkopdRga3A38Yt4v2Lp7K/857T8MiRni7ywdmpINsPAJyH4dGmth6A/h3MftX20P1qE0GFNKKaUOkTGGPy78Iwt3LuSB4x5gUv9J/s7SwTEGtnwJC56ADXMgsA8c8SM7PlhShr9z12toMKaUUkodoudznud/G/7HdWOvY/qw6f7OzoFr3AOr3rIlYYWrISIBTroHsq6FyAR/567X0WBMKaWUOgSf5H7Cw8se5oz0M7h5ws3+zs6BqS5pbg9WXQSJGTDtMds7MribdzjoxjQYU0oppQ7SiuIV/OqbXzE+YTwPHP9A1x/ComidLQVb+QY01sGw0+CYm2DISSDi79z5RUVNAx+u3sms7HyumJzOWUf091teNBhTSimlDkJeZR63fnErieGJPPLDR+gT2MffWWqbMbDpCzs+2KbPISgUxl1s24MljPR37vyirsHF52uLmJWdz7z1xdS73AyJj8Bg/JovDcaUUkqpA7S7fjc3fn4jje5GHj/lcWJDY/2dpb011NkSsIX/huK1EJkEJ98HWddARDcecuMQudyGbzeVMCt7Bx+vLqBqTyOJUX244phBTB+fypjUvoifSwc1GFNKKaUOQIOrgZ/P/TnbK7fz1GlPMTh6sL+z1FJVESx5BpY8CzUlkDQWpv8bxlwAQV209K6DGGNYmVfBu9n5vL9yJ8WVe4jqE8QZY5KZPiGVyUPiCAzoOtWzGowppZRS+2GM4YGFD7CoYBF/PP6PTEye6O8sNSvMsUNTrHoTXPUw4nTbHiz9hF7XHmxLSTWzsvOZlb2DLSXVhAQGcPKoBKaPT+XkUYmEBgf6O4tt0mBMKaWU2o9nVz/LOxvf4YZxNzBt6DR/Zwfcbtj4GSx8HDbPg6AwmHAFTP4/iB/u79x1qqLKOt5fYRvir8irQAQmD47jhh8M4fTM/kSHB/s7i/ulwZhSSinVjo+3fMwj3z3CmYPP5MZxN/o3Mw11sOI12x6s5HuI6g+n/BaOuhrCu2D7tQ5SWdfAnJxCZmXnM39jCW4DmSl9uffM0Zw9rj/9o7vXtE0ajCmllFL7kF2Uzb3f3MuRiUfywHEP+Lehd+0ueOUCyF8G/cfB+U9DxnQICvFfnjrRnkYXX64vZlb2Dj5bW8ieRjcDY8O56eRhnDs+hWGJUf7O4iHTYEwppZRqw/bK7dz6xa0kRyTzyMmPEBLox6CnqhhePg9K1sNFL9ggrBe0B3O7DYtzy5iVnc+HqwqoqG0gLiKEiycOYNr4VI4cGOP3npC+oMGYUkop1UrFngpu/OxG3Lh54tQniAmN8V9mdu+El6ZB+Xa4ZCYMO8V/eekExhjW7qxkVnY+s1fsYGdFHeEhgUzNTGba+BSOHxZPcGAXH2T3IGkwppRSSnlpcDXws3k/I78qn6enPM2gvoP8l5ldW20gVl0Cl/8P0o/zX1462PayGmav2MG7y/PZUFRFUIDwgxEJ3H3GKE7LSCI8pOeGLD33lSmllFIHyRjD/QvuZ0nBEv58wp85Kuko/2WmdBO8OA3qK+HKWZCW5b+8dJCy6no+WLmDd7N3sGzrLgAmpvfjgeljOGtsf2Ijekd7OA3GlFJKKcdTK59i9qbZ3Dj+Rs4ecrb/MlK0Fl46F9yNcNX70P8I/+XFx2rqG/l0TSHvLs/n6w0lNLoNI5IiuXPqSKaNS2FAbLi/s9jpNBhTSimlgA83f8hj2Y9xzpBzuOGIG/yXkR3ZtrF+YAj85KMeMY9kg8vNNxtKeDc7n09yCqltcJESHcq1Jwxm+vhURiVH9YiG+IdKgzGllFK93neF33Hf/Ps4Kuko7j/2fv8FBtsXwysXQmhfWzUZN9Q/+fABYwzfbdvFu8t38MGqnZRV1xMdFsz0CalMH5/CxPRYArrQlET+pMGYUkqpXm3b7m3cNvc2UiNT/TuExZav4LWLISoJrpwNMQP8k4/DtKGwknedKYnydtXSJyiAUzOSmD4+lR+MSCAkqGf1hPQFDcaUUkr1WuV15dz4uR1V//FTHie6T7R/MrLhU3jjcuiXbkvEopL9k49DtLOiltnZO5iVvYM1O3cTIHDcsHh+duoIpo5JJrKPhhvt0aujlFKqV6p31XP7vNvZUbWDZ6c+y8C+A/2TkTWz4a1rIHE0XPEuRMT5Jx8HqaKmgQ9X2zkhF20pwxgYNyCG356TwdlHpJAQ1cffWew2NBhTSinV6xhjuP/b+1lWuIy/nPAXJiRO8E9GVv4X3vkppB4Fl/0XwmL8k48DVNfg4vO1RczKzmfe+mLqXW6GxEdw+ykjmDY+hcHxEf7OYrekwZhSSqle58mVT/Le5ve4efzNnDnkTP9kYtmL8N5tkH68HVm/T6R/8rEfLrdhwaZS3s3OZ87qAir3NJIY1YcrjhnEueNTGJsa3at7QvqCBmNKKaV6lfc2vccT2U8wbeg0rj/iev9kYuG/4eO7Ydip8ONXIDjMP/nYB2MMK/MqmJW9g/dW7qC4cg9RfYI4fUwy0yekMnlIHIHaE9JnNBhTSinVaywtWMpvv/0tE5Mncv8xfhrC4uu/w+e/h1Fnw4XPQVDXaVu1paTazgmZvYPNJdWEBAZw8qgEzh2fyg9HJRIaHOjvLPZIGowppZTqFbbu3srt824nNTKVh096mODA4M7NgDHwxR/g64dg7EUw/UkI9P/XcFFlHe+vsA3xV+RVIAKTB8dx/YlDOGNMf6LDO/k69UL+vwuUUkqpDlZeV86Nn91IAAE8ceoTnT+EhTEw51ew8Ak48ko4+58Q4L9Spsq6BubkFDIrO5/5G0twG8hM6cuvzhzFOeNS6B/dtapNezoNxpRSSvVo9a56bpt7GwXVBTw79VkGRHXyYKpuN3zwc1j2PBx9A0z9MwR0/sCn9Y1u5q0vYlb2Dj5bW8ieRjcDYsO48aRhTJ+QwrDEqE7Pk7I0GFNKKdVjGWP49fxf813Rd/ztxL8xPnF852bA1QizboKVM+H4n8Mpv4FObKfmdhsW55YxKzufD1cVUFHbQFxECBdPHMC08akcOTBGe0J2AYcVjInIz4AZgAFWAT8BwoE3gHQgF/iRMWaXs/89wLWAC7jVGDPHWX8U8AIQBnwI3GaMMYeTN6WUUuqJFU/w4ZYPue3I2zh98Omde/LGevjftbB2NvzwPjjxzk45rTGGtTsrbUP8FTvYWVFHeEggUzKSOHdCKscPiyc4UKck6koOORgTkVTgViDDGFMrIm8CFwMZwOfGmAdF5G7gbuAuEclwtmcCKcBnIjLCGOMC/g1cDyzEBmOnAx8dxutSSinVy83eNJsnVzzJecPO49ox13buyRvq4M0rYcMcmPonOOamDj/l9rIaZq/YwbvL89lQVEVQgHDiiATuPmMUp2UkER6ilWFd1eG+M0FAmIg0YEvEdgD3ACc5218E5gF3AecCM40xe4AtIrIRmCQiuUBfY8wCABF5CZiOBmNKKaUO0ZKCJfz2299ydPLR/PqYX3duVdyeKph5CWz5Gs5+GLKu6bBTlVXX88HKHbybvYNlW3cBkDWoHw9MH8NZY/sTG+GnSc/VQTnkYMwYky8iDwHbgFrgE2PMJyKSZIzZ6eyzU0QSnUNSsSVfTfKcdQ3Ocuv1exGR67ElaAwc6Kc5xJRSSnVpWyq2cPvc2xkYNZB/nPwPggM6cWiGugp49SLIWwLnPQnjLvb5KWrqG/l0TSGzsnfw1ffFNLoNI5IiuXPqSKaNS2FAbLjPz6k61uFUU/bDlnYNBsqB/4rI5e0d0sY60876vVca8xTwFEBWVpa2KVNKKdVCWV0ZN352I0EBQTx+yuP0DenbeSevKYOXz4PC1XDh85A53een+M+Xm3jk8w3U1LvoHx3KtScMZvr4VEYlR2lD/G7scKopTwW2GGOKAUTkbeBYoFBE+julYv2BImf/PMC7P3Eatlozz1luvV4ppZQ6YHtce7jti9sori3m2anPkhaVtv+DfKWyEF6eDqWb4OLXYMRUnyZvjOGhT9bz+NxNnDo6iRknDGZSeiwBOiVRj3A43Sm2AZNFJFxsOH4KsBaYDVzl7HMVMMtZng1cLCJ9RGQwMBxY7FRpVorIZCedK72OUUoppfbLbdz8+ptfk12czZ+O/xPjEsZ13skr8uGFM2FXLlz2ZocEYg+8v5bH527ikkkDeeqKo5g8JE4DsR7kcNqMLRKRt4DvgEZgObYKMRJ4U0SuxQZsFzn75zg9Ltc4+9/k9KQE+D+ah7b4CG28r5RS6iA8nv04H+V+xO1H3s6U9Cmdd+KyLfDSNKgth8vfhkHH+DR5t9tw36zVvLZoG1cfm85vz8nQ6sgeSLrrcF5ZWVlm6dKl/s6GUkopP3t347v8ev6vuWD4Bfz2mN92XrBS/D28dC401tpALPVInybf6HLzy7dW8vbyfG48aSh3Th2pgVg3JiLLjDFZbW3TQUeUUkp1W4t2LuJ33/6Oyf0nc+/kezsvWClYbQMxEbj6A0jK9GnyDS43t8/M5oNVO/nFaSO45ZThPk1fdS0ajCmllOqWNpdv5mfzfsagvoP4x0mdOIRF/jJ4+XwIDoerZkO8bwOlugYXN7/2HZ+tLeLeM0dz3YlDfJq+6no0GFNKKdXtlNaWcuPnNxISEMLjpz5OVEgnTXK9dYEdRyw81gZi/dJ9mnxtvYvrX17K1xtKeODcTK44xrfpq65JgzGllFLdSl1jHbfNvY3S2lKem/ocqZFtjhPue5vmwuuXQHQqXDnb/vWhqj2NXPPCEpbmlvG3C4/goqwB+z9I9QgajCmllOo23MbNffPvY2XxSv5+0t8ZmzC2c068/mM712TcMLjyXYhM3O8hB6OitoGrnlvMqvwK/nnxBKaNS/Fp+qpr02BMKaVUt/Ho8keZkzuHXxz1C04bdFrnnDTnHfjfDEgea3tNhsf6NPmy6nqueHYR3xdW8sRlRzI1M9mn6auuT4MxpZRS3cLbG97mmVXPcNGIi7gq86r9H+AL2a/BrJsgbZId0DU02qfJF+2u47JnFrGtrIanr8zipJG+LXFT3YMGY0oppbq8hTsX8sCCBzgu5Th+dfSvOmcIiyXPwgc/hyEn2SmOQiJ8mvyO8loue2YRhbvreP4nEzl2aLxP01fdhwZjSimlurRN5Zv4+dyfkx6dzkM/eIiggE746vr2MfjkXhhxOlz0IgSH+jT5baU1XPrMQipqGnj52kkcNci3VZ+qe9FgTCmlVJdVUlvCTZ/fRJ+gPjxxyhNEhkR27AmNga/+BnP/CBnT4fynISjEp6fYWFTF5c8soq7RxWvXTWZsmm+rPlX3o8GYUkqpLqm2sZZbv7iV0tpSXjj9BfpH9u/YExoDn90P8/8J4y6BaY9BoG+/JtcV7ObyZxYBMPP6yYxK7uvT9FX3pMGYUkqpLsdt3Nz7zb2sLlnNwyc/TGa8b6cb2vuEbvj4blj8H8i6Bs78OwQE+PQUq/IquOK5RYQGBfLqdUczNKGDS/lUt6HBmFJKqS7nke8e4dOtn3Jn1p2cMvCUjj2Z2wXv3QrLX4FjboYpf7BzTvrQsq1lXP3cEqLDg3ltxmQGxoX7NH3VvWkwppRSqkt56/u3eG71c/x45I+5IuOKjj2ZqwHeuQFWvwUn/hJO/pXPA7FvN5Uw48WlJPUN5dUZR5MSE+bT9FX3p8GYUkqpLuPbHd/yh4V/4LjU47h70t0dO4RF4x546xpY9z6cej8c/zOfn2Le+iJ++vIyBsWF88qMo0mM8m2vTNUzaDCmlFKqS9iwawO/mPcLhsYM5aETO3gIi/oaeONy2PQ5nPFXOPqnPj/FnJwCbn7tO4YnRvHKjKOJjfBtr0zVc2gwppRSyu+ahrAICwrj8VMe79ghLPZUwmsXw9b5MO1ROPJKn5/ivRU7uP2NbMamRvPiTyYRHR7s83OonkODMaWUUn5V21jLLZ/fQvmecl44/QWSIzpwbsbaXfDqRZD/HVzwDIy90Oen+O/S7dz1v5Vkpcfy3NUTieyjX7WqfXqHKKWU8hu3cXPP1/eQU5rDIyc/QkZcRsedrLoEXp4ORevgRy/C6HN8foqXF+Ty61k5nDA8nqeuyCIsJNDn51A9jwZjSiml/ObhZQ/z+bbPuWviXZw88OSOO1FlAbx0LuzKhUtmwvBTfX6KZ77ezB8+WMupoxN57NIjCQ3WQEwdGA3GlFJK+cWb69/khZwXuGTUJVw2+rKOO1H5NnhxGlQVwWVvweATfJq8MYbHvtjI3z/9nrPG9ufhH48nJMi3A8aqnk2DMaWUUp1ufv58/rToT5yYdiK/nPjLjhvConSTDcT2VMKVs2DARJ8mb4zhb3PW88S8TZw/IZW/XngEQYEaiKmDo8GYUkqpTvX9ru/5xZe/YHi/4fztxL913BAWRets1aS7Aa5+D/qP82nyxhh+//4anp+fy6VHD+QP544hIKADx0VTPZYGY0oppTpNcU0xN31+ExFBETz6w0cJD+6gaYF2roCXz4OAILj6Q0gc5dPk3W7Dve+u5vXF2/jJcen85uyMjh2gVvVoGowppZTqFDUNNdz8xc1U7KngxdNf7LghLLYvgVcugD5RcNVsiBvq0+QbXW5++dZK3l6ez00nD+WOKSM1EFOHRYMxpZRSHc7ldnH313ezrmwd/zr5X4yOG90xJ8r9Bl77MUQk2EAsZqBPk69vdHP7G8v5cFUBd0wZwc0/HO7T9FXvpMHYPmwu38wb699gWL9hDI8ZztCYoUSFRPk7W0op1S39fdnfmbt9LvdMuocfDPhBx5xkw2fwxmUQM8g21u/b36fJ1zW4uPm17/hsbRH3nTWaGScM8Wn6qvfSYGwftlVu492N71LTWONZlxyRzLAYG5wN6zeMYTHDGBI9hNAgnfhVKaX2Zea6mby85mUuH305l46+tGNOsvZ9+O/Vtm3YFe9CRLxPk6+td3H9y0v5ekMJD0wfwxWTB/k0fdW7aTC2DycNOIkFly6goLqAjeUb2bBrAxvLN7KxfCOLdy6m3l0PgCAMiBrAsJhhnlK0YTHDGBQ9iOAAnYtMKdW7fZX3FX9e/GdOSjuJO7Lu6JiTrHoL3r4eUibA5W9BWD+fJl+1p5FrXljC0twy/nbhEVyUNcCn6SulwVg7AiSAlMgUUiJTODHtRM/6Rncj2yu32+Bs10Y2lNtA7cu8L3EZFwBBAUGk901vUYo2PGY4qVGpBIiOQaOU6nmMMeRV5rG6dDWrS+xjVckqRvYbyV9O/AuBAR0wIv13L8PsW2DQsXDpG7bRvg9V1DRw1fOLWZVfwSMXT+CccSk+TV8pADHG+DsPhyQrK8ssXbrU39lood5Vz5aKLZ4StKZALb8q37NPaGAoQ2KG7FXdmRSepL1xlFLdSmF1IatLV5NTksPqktXklOawu343AH0C+zAqdhRj48dy7dhriQ/zbbUhAIv+Ax/9Eob+EH78KoT4dpiM0qo9XPHsYjYWVfHYpROYktmBE5irHk9ElhljstrcpsFYx6tpqGFT+SZb3Vm+gY27bLBWXFvs2ScqOMoTmA2LGcbwfra6s1+ob4vblVLqUJTXlXtKvHJKc8gpyfF8hgVJEMP7DScjLoMx8WMYEz+GoTFDO7apxjcPw2f3w8iz4KLnIaiPT5Mv2l3HZc8sYltZDf+54ihOGpno0/RV76PBWBdVXlfeXIrmPDbs2uD5ZQkQFxrXokdnU7AWGRLpx5wrpXqy6oZq1pSusSVeTgDWVMIvCOnR6YyJG0NmfCZj4scwst/IzuvIZAzM/RN89VcYcwGc9x8I9G3Qt6O8lsueWUTh7jqevWoixwyN82n6qnfSYKwbMcZQUlvSogSt6VHbWOvZr39E/706DQyOHqw9O5VSB2WPaw/ry9Z7SrxWl6xmS8UWDPa7ITUylcw4G3RlxmWSEZfhvx+DxsAn98GCx2DC5XDOv8DH7dC2ldZwydML2V3bwAvXTOKoQVo7oXyjvWBMG/B3MSJCQngCCeEJHJtyrGe927jZUbWjRQnaxvKNLNy5kAZ3A2A7HAyMGugJ0ppK0Qb2Hag9O5VSNLob2VS+yTaud9p6bdi1gUbTCNiS+DHxYzh98Omekq/Y0Fg/59rhdsOHv4Clz8Gk6+H0v0CAbztDbSyq4rJnFrKn0c1r101mbFq0T9NXal+0ZKyba3A3sH33djaUb2hul7ZrA9sqt+E2bsD27BwcPbi504ATrKVGas9OpXoqt3Gzbfe2Fg3s15Wto85VB0BUSJSnxKsp8OqyHYlcjTD7ZljxOhx3G5z6O/BxPtfu3M0Vzy4ChFdmTGJUcl+fpq9Uh1RTishI4A2vVUOA3wAvOevTgVzgR8aYXc4x9wDXAi7gVmPMHGf9UcALQBjwIXCb2U/GNBhr3x7XHrZUbGkxPtrGXRvZUb3Ds09YUBhDo4e2GHpjWL9hJIQldM0PZKVUm4wxFFQXNDewL8lhTekaKhsqAfu/Pjp2tG3jFWcb2A+IGtA9/s8b6+Ht62DNu3DyvXDinT4PxFbmlXPlc4sJDQrk1euOZmiCtslVvtfhbcZEJBDIB44GbgLKjDEPisjdQD9jzF0ikgG8DkwCUoDPgBHGGJeILAZuAxZig7F/GWM+au+cGowdmqr6KjZVbPK0R2tqm1ZaV+rZJyokqkUJWlOgFhMa47+MK6U8SmtLPe27mtp6ldWVAbYkfGS/kc3tvOIzGRI9hKCAbtgqpaEO/nsVfP8xTPkDHHuLz0+xbGsZVz+3hOjwYF6bMZmBcb4dHkOpJp3RZuwUYJMxZquInAuc5Kx/EZgH3AWcC8w0xuwBtojIRmCSiOQCfY0xC5zMvgRMB9oNxtShiQyJZFzCOMYljGuxflfdrhYlaBvLN/LRlo+o/L7Ss098WPxeQ28MjRlKRHBEZ78MpXqNyvpKz1ASTQHYzuqdgG0nOiR6CCeknuAZUmJEvxGEBIb4Odc+UF8NMy+FzfPgrL/DxBk+P8W3m0qY8eJSkvqG8uqMo0mJCfP5OZQ6EL4Kxi7GlnoBJBljdgIYY3aKSNPgLKnYkq8mec66Bme59XrVifqF9mNi8kQmJk/0rDPGUFRTtFengbe+f8vT7gRsb6umwKwpUBscPZg+gb4d90epnq62sdbTs7GprVfu7lzP9gFRAxifMJ7LRl/GmPgxjI4dTXhwDyzJqdsNr/0Iti+C6f+G8b6fz3Le+iJ++vIyBsWF88qMo0mM0p7oyn8OOxgTkRBgGnDP/nZtY51pZ31b57oeuB5g4MCBB5FLdShEhKSIJJIikjgu9TjPerdxk1+Z75kGqukxf8d8Gt22V1ZTz86mErSmKs+BUQO7Z3WJUj7W4G5gw64NLYaU2FS+yTOlWmJ4ImPixnDO0HM8Deyj+/SC3n01ZfDK+VCwCi54Fsac7/NTfLy6gFte/44RSVG8fO3RxEb0gJJE1a354lvxDOA7Y0yh87xQRPo7pWL9gSJnfR7gPbtqGrDDWZ/Wxvq9GGOeAp4C22bMB3lXhyBAAhjQdwAD+g7ghwN/6Fnf4G5g2+5tLcZI+37X93y29TPPmEVhQWEcEX8E4xPHMz5xPEckHEHfEO21pHo2l9tF7u5cT9CVU5LDurJ11LvrAYjuE82YuDGcNOAkTwP7hPAEP+faD6qK4KXpULoBfvwKjDzD56eYvWIHP3sjmyPSonnhJ5OIDtNhf5T/+SIYu4TmKkqA2cBVwIPO31le618TkX9gG/APBxY7DfgrRWQysAi4EnjUB/lSnSw4IJihMUMZGjPU9qV11DbWenp25pTmkF2UzdOrnsZt3AjC0JihNjhLsAHawKiB3aOXl1JtMMaQX5XfYkiJNaVrqGmsASA8KJyMuAwuHX2pp3djamSq3vMV+fDSuVCRZyf8HvrD/R9zkN5cup27/reSSemxPHv1RCL7aCm96hoOqzeliIQD24EhxpgKZ10c8CYwENgGXGSMKXO23QtcAzQCtzf1mBSRLJqHtvgIuEWHtujZahpqWFWyiuVFy8kuzmZl0UpPN/zY0FjGJYxjfOJ4JiROICMuQ9ufqS6ruKa4RRuvnNIcyveUAxASEMKo2FEt5mxM75tOoI9Hje/2duXCi9NsFeVlb8KgY/d7yMF6eUEuv56VwwnD43nqiizCQvQ9UJ1Lp0NSXZ7buNlUvons4myyi+xjW+U2wHbVz4jLYELCBE/1ZnxYvJ9zrHqjij0Vnvkam/4W1diWGIESyNCYoZ5pg8bEj2F4zHCCfTxvYo9TssGWiNVXw+VvQ9pRPj/F019t5o8fruXU0Uk8ftkE+gRpIKY6nwZjqlsqrS1lRfEKT4CWU5LjaWOTGpnKhMQJnqrNYTHDtLRB+VRNQw1ry9Z62nitLl3N9srtnu3pfdNbDKI6MnYkYUE6NMJBKcyxgZgxcOUsSB7j0+SNMTz6xUb+8en3nDW2P/+8eDzBgTrriPIPDcZUj1Dvqmdt2VpPydnyouWewWojgiOaOwYk2I4BfpvMWHUrxhgKawrZXLGZzeWbWVe2jpzSHDZXbPZMKdY/or+nxCsz3k6WrR1PDlP+d7bXZFAoXDkbEkb4NHljDH+bs54n5m3i/CNT+esFRxCkgZjyIw3GVI9kjCGvKo/somxbglaUzfe7vsdgEITh/YZ7Ss7GJ44nLTJNG0n3Yi63ix1VO9hcsZlNFZvYXL7ZBmAVm6luqPbsFxsa2zxnY/wYMuIytFrc17YthFcvgrAYG4jFDvZp8sYYfvfeGl74NpfLjh7IA+eOISBA//eVf2kwpnqNqvoqVpasZEXRCpYXLWdlyUrPF21caJynU8C4hHFkxGX0jJHKVQsNrga2VW6zQVf5Jk+JV+7uXPa49nj2SwhLYEjMEIZED2Fo9FDPcmxorAbtHWnzPHj9EuibYqsmo9P2e8jBcLsN9767itcXb+ea4wbz67NH6/upugQNxg5FdSmUrIfIJIjqDyE9cJTrXsDldrGxfCMrim1wll2UTV6VnfAhJCCEzPhMxieMZ1ziOMYnjCcuLM7POVYHqraxltyKXE/QtaViC5sqNrF993YaTaNnv9TIVIZEO0FXzFAGRw9mSMwQrWb0h+/nwBtXQNxQuOJdiEryafKNLje/fGslby/P56aTh3LHlJEaiKkuQ4OxQ7H2PXjj8ubnffpCVHJzcBbl/PU8T7aPEJ2nsasrqS3xtDvLLs5mTekaGtwNAAyMGuip1hyfMJ6hMUMJEG1n4k+V9ZWe0q2masVN5ZvYUbXDM5hwoAQyIGoAQ2OG2sDLKeVK75veM6cL6o5y3oX/zYCkTLjiHQiP9Wny9Y1ubn9jOR+uKuCOKSO4+YfDfZq+UodLg7FDUV0KBSugsqD5UVXQ8rlXlYdHSFRzYNYieGt67vzto43Lu4o9rj2sKV3TIkArqysDICo4iiMSmmcMGBs/VidG7yBldWXNJVxe1YtFtUWefUICQkiPTmdo9FAGxwy21YvRQxjUd5AOIdGVrZgJ7/4fpE2Ey/4Lob6d1qmuwcVNr37H5+uKuO+s0cw4YYhP01fKFzQY6wjGQF15+8Fa0/PGur2PD4lsGZy1DtaaHn2iOv2l9XbGGLZXbie7ONtTtbmpfBMGQ4AEMLLfyBaD0vaP6K9VIQfI03PRKeXybkjfNFAq2FHqvUu4mkq8UiNTdQiT7sTVAMtegA/vhMEnwMWv+/yHaE19I9e/tIxvNpbwh+ljuHzyIJ+mr5SvaDDmT8ZAXUX7wVrTo7F27+ODI1oGZy2qRr2qSvtEgQYEHWZ3/W5WFq/0lJytLF5JrfN+JYYlMi5xnGfcs1Gxo3p9KU1Tz8VNFZtatOlq3XMxuk90i8bzTctJ4Uka4HZHjfWwYznkfg1b58O2RdBQDcOnwI9egmDfjsNWtaeRa55fwtKtZfz1wnFceJRvOwMo5UsajHUHxsCe3QdW0tZQs/fxwRFttGNr43mfvhq0+UCju5ENuza0mDFgR7Wd375PYB8y4zJtcJY4nnEJ4+gX2s/POe4YTT0XvasVN1fs3XMxMSyxRbWi9lzsIRr3QN5SG3jlfgPbFzf/qEwYDenHQfrxMOps8PEPlIqaBq58fjE5+RX88+LxnH1Eik/TV8rXNBjrSYyBPZUHVtLmVQLhERzefrDW9Dw0WoO2g1RYXejptbmieAVrS9d6evWl9033dAqYkDiB9Oj0btUxoKnnYuvxufbXc3FIzBAGRw/Wnos9RUMt5C2B3Pk2AMtb0twMI2kMDHKCr0HHQkTHjc1WWrWHK55dzMaiKh67dAJTMpM77FxK+YoGY71VU9C2v5K2+qq9jw0KO7CSttAYDdr2oa6xjtUlq8kuzmZFkZ3WqaldVN+Qvi3anWXGZXaJXn+tey42lXhpz8Veqr7alnY1lXzlLwNXPSDQ/wgYdLwt/Rp4jM97R+5L0e46LntmEdvKanjqyix+MCKhU86r1OHSYEy1b08lVBa2CtZ2QlVhy+CtvnLvY4NCD6ykLaxfrw/ajDHk7s71zBiwvGg5mys2AzbAGRk7ssV8m8kRHfdrv6nnondD+i3lW7TnYm+3p9K289r6jS392vEduBtBAqH/OBt4DToeBk62o+d3svzyWi57eiFFlXt49qqJHDNUxwVU3YcGY8o39lR5BWhNwdpOG8h5B297du99bGAfG5wljYHR58DIM2yA1stV7KnwTOWUXZzN6pLVno4ByRHJLaZzGtFvBMEBBx4EefdcbGpIrz0XVQt1FXZqotxvbOnXjmwwLggIgpQJTrXjCTDwaL/37N5aWs2lTy9id10DL/xkEkcN0s8P1b1oMKY6V321Uw3aKlir3Albv4Xd+fbDfvCJNjAbdTZEJvo7111Cg7uB73d932LMs4LqAgDCgsIYEz/GE6CNSxhHdJ9oXG4X+VX5e03/s2X3Fu25qFqq3QVbFzjB1zdQsAqMGwKCIS3LCb6OgwFHd6kBrDcWVXHZMwupb3Tz8rVHMybVt+OUKdUZNBhTXYcxkP8drJ0Fa2bDri2A2Aa/o8+xDx/PVdfdFVQXeAKz7KJs1pWtw2VcAKREpFBSW0K9u96zv/ZcVB7VpbbEa+t8W+1YuBowtqQ6baJT7XicXe6iU76t3bmby59ZhIjw6oyjGZmsYy+q7kmDMdU1GQOFObB2tp1+qmiNXZ9yJGRMg9HT7Bx2qoWahhpySnPILsrm+13fkxyR7Am6tOdiL1dV1Bx4bZ3f/D8VFAYDJtoqx0HHQepREBzq37wegJV55Vz53GJCgwJ59bqjGZqgM5eo7kuDMdU9lGy0JWZr37MDRwIkZjYHZomje30nAKVaqCxobu+V+w2UfG/XB0fYdl5NQ02kHAlBIf7N60FamlvGT55fQnR4MK9fN5kBsV2z5E6pA6XBmOp+yrfB2vdtqdm2hYCB2KHNgVnKBA3MVO9TkeeUejm9Hcs22fUhUbaHY1Nvx5TxPh9ktTN9u7GEa19cSv/oUF697mj6R/t25H6l/EGDMdW9VRbCOicw2/K17e0VPaC5jdmAo0F7/ameaNdWr2rHb2BXrl3fJ9q2s2xq85V8BAQG+TWrvjJ3fRE3vLyM9LgIXp4xicSorl+dqtSB0GBM9Rw1ZbD+IxuYbfrCDkAZkQijz7aBWfoJ3bpEQPVixtgOLU3tvXK/gYrtdltYPxt0NfV2TBrTI3+AfLy6gFte/46RyVG8dM3RxEZ0r6pVpdqjwZjqmep2w4ZPbGC24VM7Z2doDIw801ZnDjm5WzRSVr2UMVC6qXlS7dz5UGnnNyU83in5Ot4GYIkZENB9ps86FLOy8/n5mys4Ii2aF34yiegw/VGlepb2grGeUa7dARZuLuWet1cxNCGCoYmRDE2IZJjzVz8kuojQvjD2QvtoqIWNn9vG/+s+gBWvQUgkDJ9iA7Nhp0Ef7Yml/MgYKF7f3N5r63w7Fh/Y0t3045vbfCWM7FVtIt9csp273l7JpPRYnr16IpF99KtJ9S56x+9DaHAgo/tHsamomq++L6He5fZsi4/sw7DEiBYB2tDESFKiQ3UcJ38JDnOqKs+GxnrI/cqOY7buA8h5207bNPQUG5iNmKqj/6uO53ZD8drm9l5bv4XqYrstKsUOetzU2zFuWK8Kvry9tCCX38zK4YTh8Tx1RRZhIT2v+lWp/dFqygPQ6HKTt6uWjUVVbCq2j41F9rG7rtGzX3hIIEMSIhiW0BygDUuMZFBcOH2C9APGL9wu2LbABmZr37PVQAFBMPgHNjAbeRZE6kTDygfcbjuoalN7r63fQm2Z3RY9oLm9V/rx0G9wrw2+vD311Sb+9OE6TstI4rFLJ+jnpOrRtM1YBzHGUFJV3yJA21RczaaiKvLLaz37BQYIA2PDbZWnE6Q1lapplWcncrvtxMdrZtl2ZrtyQQJg4LE2MBt1NkSn+juXqrtwNULByub2Xtu+tXM9AsQMsp1Jmno79hvk37x2McYY/vX5Rh7+7HvOOqI///zxeIIDe3abOKU0GPODmvpGNhdX20CtqIqNxVVsKqpmS0n1XlWeQxMiPNWdwxJtsNa/bygBAfrLucMYY0sxmkrMitfa9alZzlhm50DsEP/mUXUtrgbYuaJ5kNWtC6C+0m6LHdrc3iv9OJ3Sqx3GGP46Zz3/nreJC45M468XHkGgftapXkCDsS7E5TZsL6vxKkmzpWkbi6qoqG3w7NdU5Tk0IdJWe2qVZ8cq2eCUmL0HO7PtuqSxNijLmAYJo7RaqbdprLczQTT1dty2CJomXo8f0dzea9Bx0Le/f/PaTRhj+N17a3jh21wuO3ogD5w7Rn90ql5Dg7FuwBhDaXV9c4BWVO2UprWs8gwQGBgb3qLjQFPAFh2uVZ4+sWurHWR2zWzYvggwEDe8OTDrP14Ds56msd6O6VW22QnAvoHti6HR+d9LzGhu8zXoOIhM9G9+uyGX23Dfu6t4ffF2rj1+MPedNVo7PKleRYOxbq51lWdTSZpWeXaCyoLmwCz3G2f0/4HNgVnapB4//lOP0VBr2wmWbYayLc5f51GxHUzT/5LYQVWbAq9Bx0FEnD9z3u01utzc+dZK3lmez80nD+MXU0ZoIKZ6HQ3GeijvKk/vDgStqzzDggMZmtiyynNoQiTp8VrleVCqS+H7j2xgtnmuHf0/Msk2/M+YZtsL9ZApabqtPZUtA61dW5qf785vuW9otG3rFTsEYgc7f4fYMb506BOfqW90c9vM5Xy0uoA7p47kppOH+TtLSvmFBmO9zCFXeTa1TdMqz/1rGv1/zSzY+Jkd/T+snx0qY/Q5MPRkCOrj71z2TDVlLYMsz2MLVBe13DcioTnIanr0G2yDr/BY/+S/lyjcXcfybbt4ddE2vt5Qwq/PzuDa4wf7O1tK+Y0GY8rjYKs8m4Kzpg4EWuXZhvoa2OSM/r/+Y9hTASFRdnDZ0efA8NMgJMLfuew+jLGDo7ZVnVi2GerKW+7fN7U5wGoReA2GPlF+eQm9TV2Di9X5FWRvL2f5tnKWb9vFjoo6APoEBfCbczK47Ggd3kP1bhqMqf1yuQ15u2o8pWntVXkOad0uTas8mzXWw5Yv7Thm6z6AmlI7+v+wU2F00+j/Mf7Opf+53VC5s2WQtWtLcwBWX9W8rwTYQVO9gyxPKVe6nX1BdRpjDFtLa5zAaxfLt5ezZsduGt32u2RAbBjjB/RjwoAYJgyMISOlr342KIUGY+owNFV5eo+V1hSstVXl2XKKqAiGJUT13ipPV6MdCHTte87o/zshIBiG/MAGZqPOgoh4f+ey47gaYXdey2pET+CVC411zfsGBNuBUduqUowZCEEhfnsZvd3uugZWbq/wBF7Lt+1iV439gRYeEsi4NBt0TRjYj/EDYkiI0up5pdqiwZjqELX1Lk/ngaaZBzYVV7G5pJr6Ru8qzxBPe7TxaTGcMjqRuMhe9oHtdkP+Mlg7y3YAKN9qS3wGHWcDs9FnQ98Uf+fy4DXW29fSVpVi+VZwN08XRlBoyzZb3kFXdBoEaOmJv7nchg1FlZ6qxuXbytlYXIUxdjSXYQmRnsBrwsAYhidG6YCtSh0gDcZUp2qq8vRUdzodCJqqPAMEJqbHMjUzmSmZSaT1C/d3ljuXMVCwylZlrpkNJevt+rSJTmB2jg1Wuor6Gq8hIVpVKVbkeQ0JgW0r5wm0WgVckck6DEgXU1y5p7m6cVs5K/PKqa53AdAvPNgGXQNs8HXEgGj6hvbSUm6lfKDDgjERiQGeAcYABrgGWA+8AaQDucCPjDG7nP3vAa4FXMCtxpg5zvqjgBeAMOBD4Dazn4xpMNb9GGPI2bGbOTkFzMkp4PtC2y5oTGpfpmYkM3VMMsMTI3vf+EPF621gtvY9O90OQPJYGH2uDcwSR3V8Hup2t2q35VXSVbmz5b5h/fbRQ3GIrXbtbe9fN7Gn0cWaHbttqZcTgOXtsk0NggKEjJS+nsBr/IAYBsWF977/RaU6UEcGYy8CXxtjnhGRECAc+BVQZox5UETuBvoZY+4SkQzgdWASkAJ8BowwxrhEZDFwG7AQG4z9yxjzUXvn1mCs+9tSUu0JzJZvKwdgcHwEUzKTmJqZzPi0mN7Xc3NXbnMbs+2L7Lr4ETYoGz0N+o87tGDHGKjd1Xb7rbLNUFPScv/IpOYAq0Up12Adg6sbMMaQt6vWE3Rlby8nJ3+3p8d0SnSoJ+iaMDCGManRhAZrNbFSHalDgjER6QusAIZ4l2KJyHrgJGPMThHpD8wzxox0SsUwxvzZ2W8OcD+29GyuMWaUs/4S5/iftnd+DcZ6lsLddXyyppBPcgpYsKmURrchqW8fTsuwgdnkIXEEB/ayKq7dO+3o/2tnQ+58O/p/zECnKnOardb0rvYzBqqK9h4Koqm0q66iZfp909quUuw3GPpEdu5rVYelak8jK/PssBJNw0uUVO0BIDQ4gCPSYjy9G8cP6EdydKifc6xU79NRwdh44ClgDTAOWIYt3co3xsR47bfLGNNPRB4DFhpjXnHWPwt8hA3GHjTGnOqsPwG4yxhzdnvn12Cs56qoaeCL9YXMWV3Il98XU9vgom9oEKeMTmJqZhInjkggPKSXjXRfXQrrP7SB2aa54G6wbbCGnWrHNStzBkFtmsgaQAIhZkDb1Yn9BumQEN2U223YVFzVorrx+8JKnJElGJIQ4ZR42fZeI5Ojet8PGaW6oPaCscP5RgsCjgRuMcYsEpFHgLvby0cb60w76/dOQOR64HqAgQMHHlxuVbcRHR7MeRPSOG9CGrX1Lr7eUMzHOQV8vraId5bnExocwAnDEzg9M5lTRicSE94Lhj2IiIMjr7CPugr4/hPbM3P9BxAebwOs9BNaVivGDIRAbXDd3ZVV15O9fRfZTvCVva2cyj22l2rf0CDGD+zH1Mxkp9Qrpnf8PyjVwxxOMJYH5BljnIYtvIUNxgpFpL9XNWWR1/4DvI5PA3Y469PaWL8XY8xT2NI4srKyumc3UHVQwkICmZKZzJTMZBpcbhZvKWNOTgGf5BTy6ZpCAgOEyUOcnpkZyb2j+iU0Go64yD5Uj9LgcrN25+4WI9nnltYAdiy/Ucl9mTY+xTO0xOC4iN7XrlKpHuhwG/B/DcwwxqwXkfuBpjlfSr0a8McaY34pIpnAazQ34P8cGO404F8C3AIswjbgf9QY82F759Zqyt7N7TaszK/wdADYXGyr58YNiGGq0wFgaIK2e1Jd286K2hZjeq3Kr2CPM0ZfQlQfjhzYXN04Ni2691XPK9WDdGRvyvHYoS1CgM3AT4AA4E1gILANuMgYU+bsfy92+ItG4PamHpMikkXz0BYfYas+dWgLdcA2FlUyJ6eQOTkFrMyzDdWHJUZ6ArOxqdHaTV/5VW29i1X5FZ7Aa/n2XRTuto3sQ4ICGJsa3Ty0xMAYUqJD9Z5VqgfRQV9Vr7KjvJZPcgqYk1PI4twyXG5DSnSoU92ZxKT0WIK0QbPqQMYYtpRUe4Ku5dvKWVdQictpZT8oLrzFmF6j+/clJEjvSaV6Mg3GVK+1q7qez9YWMienkK83FLOn0U2/8GCnZ2YyJwyP1/GV1GGrqGkgO6+5ujF7ezkVtXb+xsg+QYwfEOMZ02v8gJjeNx2YUkqDMaUAqvc08uX3xczJKeCLdUVU1jUSHhLID0YkMDUzmZNHJRIdpr0PVfsaXW7WFzbN32hLvpraLIrAyKQoT+A1YWA/hiZE6vyNSikNxpRqrb7RzYLNpczJKeDTNYUUV+4hOFA4Zmg8UzOTOC0jicSoXtAzU+1X0e46vnOCruxt5azMq6C2wc7fGBcR0jxxttPIPkrnb1RKtUGDMaXa4XYblm/f5ekAsLW0BhE4cmA/TweAQXER+09IdXs19Y2s3dk8f2P2tnLyy+38jcGBQkZKtGck+yMH9iOtX5g2sldKHRANxpQ6QMYY1hdWMme1DczW7NwNwKjkKKZkJjM1M4mM/n31C7gb29PoYltpDVtKqsktrWZLSfOjqXcjQGpMWHOp18AYMvr31faFSqlDpsGYUodoe1mNZ5DZJVvLMAYGxIYxJSOZqZnJHDWon7YH6oIaXW7yy2vZXFJNbknLgGtHea1n6iCA2IgQBsdHkB4XwZCECIYnRjJ+YIxWUyulfEqDMaV8oKRqD5+tsSVm8zeWUu9yEx8Zwqmjk5g6Jpljh8bRJ0hLTjqL220o2F1Hbkl1y6CrtJrtZTU0uJo/26L6BJEeH2GDrvgIhjh/B8dFEB2ubbyUUh1PgzGlfKyyroF5623PzLnriqiudxHZJ4iTRyUyNTOJk0YmEtlHR0s/XMYYSqrqbXVisQ20thTb6sXc0mrqGtyefUODA0iPaw64Bns94iJCtGpZKeVXGowp1YH2NLr4dmMpH68u4LO1hZRW1xMSFMDxw2zPzFNHJ+m4UvtRUdNgA62SKraU1HhKuXJLqj2TYgMEBQgD48IZHLd3KVdy31Cdp1Ep1WVpMKZUJ3G5DUtzyzw9M/PLawkQyEq3k5lPzUwirV+4v7PpFzX1jU6AVeMJuraUVJFbWkNZdb1nPxFI6xdm23A5gVZT0JUaE6azJyiluiUNxpTyA2MMOTt2e6ZmWl9YCUBmSl8nMEtmRFJkj6o+O9CeigBJffu0qEpsakA/IDZc294ppXocDcaU6gJyS6qZk1PAnJwCvttWDkB6XDhTM5OZkpnMhAEx3aKa7WB7KqbHhTM4PpLB8fZvenw46XERRGibOqVUL6LBmFJdTNHuOj5xemYu2FRKo9uQGNWH0zLsILOTh8T5deLopp6K3oFWrvZUVEqpQ6bBmFJdWEVtA3PXFTEnp4B564upbXARFRrEKaMSmZqZzA9GJhAe4vtSpIPpqdgnKMBTlTg4wQZagxPs8/hI7amolFL7o8GYUt1EXYOLrzeUMCfH9swsr2mgT1AAJzqTmZ86OpGY8JCDSvNweio2tefSnopKKXV4NBhTqhtqdLlZvKXMzgCwppCdFXUEBghHD4512pkl0T86DDi4noqpMWEtG85rT0WllOpwGowp1c0ZY1iZV+HpALCpuBqAEUmRVNQ2tNlTsal3YtNAqIPjbU9FnV9RKaU6nwZjSvUwG4uqmJNTwKItZSRE9tGeikop1cW1F4zpJ7ZS3dCwxEiGJQ7jppP9nROllFKHSxuIKKWUUkr5kQZjSimllFJ+pMGYUkoppZQfaTCmlFJKKeVHGowppZRSSvmRBmNKKaWUUn6kwZhSSimllB9pMKaUUkop5UfddgR+ESkGtnbwaeKBkg4+R2+j19T39Jr6ll5P39Nr6lt6PX2vM67pIGNMQlsbum0w1hlEZOm+pi5Qh0avqe/pNfUtvZ6+p9fUt/R6+p6/r6lWUyqllFJK+ZEGY0oppZRSfqTBWPue8ncGeiC9pr6n19S39Hr6nl5T39Lr6Xt+vabaZkwppZRSyo+0ZEwppZRSyo80GFNKKaWU8qMeGYyJiBGRl72eB4lIsYi876P07xeRO3yRVlcnInEiku08CkQk3+t5iA/Pc5Kv3h9/EpGHReR2r+dzROQZr+d/F5GfH0A66SKyuoOy2XSOqo5MvyO1c1+Wi8iaTjj/1SLyWEefpysREZfXNc8WkfQ29vlQRGLaWN9rPjNbE5F7RSRHRFY61+3odva9WkRSfHDOXBGJP9x0uouDucYHkWan3rNBnXWiTlYNjBGRMGNMLXAakO/nPHVLxphSYDzYmxOoMsY85M88dXHfAhcB/xSRAOxAgn29th8L3O6HfPUo+7ovnQDhkIN6EQkyxjT6Io89UK0xZnxbG0REsG2Qz+zcLHVtInIMcDZwpDFmjxMgtfcj9mpgNbDjIM7Rq+/ZQ7jGXVKPLBlzfASc5SxfArzetEFEYkXkXSeKXigiRzjr7xeR50RknohsFpFbvY65V0TWi8hnwEiv9deJyBIRWSEi/xORcBGJEpEtIhLs7NPX+aUS3BkvvKOJyAsicqHX8yqv5Tud67FSRH7nrIsQkQ+ca7RaRH7srD9dRNaJyDfA+V5pTBKRb0VkufN3pLP+axEZ77Xf/Kb3rguZjw24ADKxH6yVItJPRPoAowFE5EsRWeaUnPV31h3lXKMFwE1NCTq/lt8WkY9FZIOI/NVr2xQRWSAi34nIf0Uk0ln/oIiscd6Hh5x1g519l4jIA15pRIrI504aq0TkXGf9AyJym9d+f/T+n+jCAkXkaeeX8iciEgbg/F9nOcvxIpLrLF/tXLv3gE9EpL+IfOX8wl4tIic4+/1ERL4XkS+B45pOJiLniMgi5379TESSRCTAea8SnH0CRGSj9KDSCrGlt2tF5AngO2CAeJXI6GcmAP2BEmPMHgBjTIkxZoeI/Ma5BqtF5CmxLgSygFedey+s1fXMEpF5zvL9znGfAC+JLSn+xLkH/wNIUwbEftctc/4frnfWXSsiD3vtc52I/KOzLoqP7esat3ftut73vDGmxz2AKuAI4C0gFMgGTgLed7Y/CvzWWf4hkO0s348t2eiDLdEoBYKBo4BVQDi2lGMjcIdzTJzXef8A3OIsPw9Md5avB/7u7+vig+t6P3AH8AJwoff1dv5OwXYPFmyg/z5wInAB8LTX/tHO+7IdGO7s/6bX+9MXCHKWTwX+5yxfBfzTWR4BLPX3NdnHdcoFBgI/BW4AHgDOxH6BL3DusQRn3x8DzznLK4EfOMt/A1Y7y1cDm72u21ZggHOPfgVEOPvdBfwGiAXW09xbOsb5Oxu40lm+yet9CwL6Osvxzv0tQDrwnbM+ANjkfb93lUfTfekspwONwHjn+ZvA5c7yPCDL63Xmel3fPCDWef4L4F5nORCIwn7gbwMSsL+65wOPOfv087rWM3D+14HfArd7/W/8z9/X6jCvswv7WZoNvONcazcwudW9H49+Zja9vkjnen0PPEHz/3es1z4vA+e0vke9r6eznAXM87rnlwFhzvN/Ab9xls8CjNdxTfd1GPbHYRwQ4fw/BzvbvgXG+vt6+fgat3ftutz3fI8tGTPGrMR+WFwCfNhq8/HYfwCMMV8AcSIS7Wz7wBizxxhTAhQBScAJwDvGmBpjzG7sl1qTMWJLbFYBl2FLQwCeAX7iLP8E+6b1dFOcx3LsL+VR2GBrFXCqiPxFRE4wxlQ427YYYzYYeye/4pVONPBfsW2mHqb5mv4XONv55XENNijsippKx47FBl8LvJ7nA2OAT0UkG7gPSHPuvxhjzJdOGi+3SvNzY0yFMaYOWAMMAiYDGcB8J62rnPW7gTrgGRE5H6hx0jiO5hJi7/QF+JOIrAQ+A1KBJGNMLlAqIhNw3ldjqwe7ui3GmGxneRn2c2B/PjXGlDnLS4CfiK3+HGuMqQSOxn6YFxtj6oE3vI5NA+Y4nwF30ny/Pgdc6SxfQ/f/DKg1xox3Huc567YaYxa2sa9+ZgLGmCrsl/z1QDHwhohcDZzslKauwhYIZO47lX2abWwzHLA/el9xzvkBsMtrv1tFZAWwEPsjbrgxphr4Avt5OgoblK06hDz4XTvXuD1d7nu+p7YZazIbeAhbKhbntV7a2LdpwLU9XutcNF+jfQ3I9gI2Ml7h3AAnARhj5jvF+D8AAo0xHdoYu5M14lRxi4jQXD8vwJ+NMf9pfYCIHIUtHfqzU7Q+m31f0weAucaY88S2AZoHYIypEZFPgXOBH2F/7XRF32IDr7HYX6LbsaUtu7EfgKnGmGO8DxDb6Lm9Qf/aui8FG0Rc0npnEZkEnAJcDNyM/cBnH+e4DFvic5QxpkFs9V2os+0ZbMlRMja46A5aX6swZ9lz39L8+ppUNy0YY74SkROxJQwvi8jfsO/dvt6fR4F/GGNmi8hJ2F/eGGO2i0ihiPwQG8xddqgvqAurbmebfmYCxhgX9jNsnvNl/lNszU2Wc4/cz973Y5MDumebTtX6YOd+PBU4xvn8nEfL/+1fAevo5oFvG9f4Ktq/dl3ue77Hlow5ngN+30bE/xXOB6Nzs5Y4kfC+fAWc59ThRwHneG2LAnY6pTWtP2xfwpZEdOsbvQ252F8iYAOjpjryOcA10txuKVVEEsX2DqoxxryCDY6PxH4ADBaRoc6x3gFFNM0dLq5ude5nsEXyS7xKMrqa+dgGpWXGGJeTzxjgGGyJSoLYRqeISLCIZBpjyoEKETneSeNAvrgXAseJyDAnrXARGeFc/2hjzIfYzgLjvfJ1cRvpRwNFTiB2MrZ0rck7wOnAROz7253l0nzfXrivnURkEPZ6PA08i71fFwEnOW1zgrGdNJp4369XtUruGWyJxZvOF0ZvoZ+ZgIiMFJHhXqvGY5sQAJQ4/6ve92Il9vo0yaX5nr2gnVN5f6edga06B3tv7nICsVHY0nQAjDGLsCVll+LVprq72cc13sqBX7smfr1ne3TJmDEmD3ikjU33A8871TI17P0B2jqd70TkDWy99Fbga6/Nv8Z+UG/FVsd5/yO9iq1f7rY3+j48DcwSkcXA5zi/0Iwxn4jIaGCBLTCjCrgcGAb8TUTcQAPwf8aYOrGNST8QkRLgG2z1HcBfgRfFDgHxhfeJjTHLRGQ3XfvDehW2LcJrrdZFGmOKxDbU/ZdTNRkE/BPIwRZzPyciNRxA4GOMKXZ+pb0utnMA2GrPSuz7E4otPfuZs+024DWxjfL/55XUq8B7IrIUe4+v8zpHvYjMBcp7QDDxEPCmiFxBq/uqlZOAO0WkAXsPX2mM2emUYCwAdmKr4QOd/e/HVqvnYwPkwV5pzcbeq135fvU5/cz0iAQedUq+G7HtkK4HyrGvPRdbLd7kBeBJEanF/nj7HfCsiPwKe8325XfYz4HvgC+x7RsBPgZucL7r1mPvT29vYttX7qL72tc1Hs2BXTvA//esTofUgZwv3XONMVf4Oy89hVPKNg8YZYxx+zk7PZ7Y4Tm+Ay4yxmzwd366G7G9Nx82xpzg77x0B/qZ2bnEju34sDHmc3/npbvy1T3bo0vG/ElEHgXOwLaTUj4gIlcCfwR+roFYxxORDGyP2Hc0EDt4InI38H/0zLZiPqefmZ3HKUVaDKzQQOzQ+fKe1ZIxpZRSSik/6ukN+JVSSimlujQNxpRSSiml/EiDMaWUUkopP9JgTCmllFLKjzQYU0odMBGJEZEb/Z0Pb2In+k7xev6M0xPUV+lP905PRH4vIqf6Kv02zne7iIR3VPpKqa5He1MqpQ6YMz3V+8aYMa3WB/prUFhnipc7jDFLOyj9F7Cv+a2OSL+N8+Vip8op6YzzKaX8T0vGlFIH40FgqIhki8gSEZkrIq9hR6VGRN4VkWUikuPMsICzvkpE/igiK0RkoYgkOesvEpHVzvqvnHXpYifl/c55HOuVzi9FZJWz/4POgItZwKtOnsJEZJ4z2Coicomz/2oR+cv+8tOac+5p2BkkskVkqIi84JwXEckVkT+JyAIRWSoiR4rIHBHZJCI3eKVzp3O9VorI75x1ESLygZOH1SLyYxG5FUgB5jozHyAi/3bSzmk69kDPLSInichXIvKOiKwRkSedgXyVUl2JMUYf+tCHPg7oAaQDq53lk7BTYQ322h7r/A3DTpIe5zw3wDnO8l+B+5zlVdiJ0wFinL/hQKizPBxY6iyfgZ2EPbzVueZhS5Lwfo4NarZhJ0EPwk6BNL29/OzjNb8AXNjWc+x0Nv/nLD8MrMROlZKAnd8SYArwFHZqqgDsQLonYufLe9or3WivNOPbuKaBzms74iDOfRJQBwxxjv/U+7XoQx/66BoP/YWklDoci40xW7ye3yoiK7Bz4A3ABlMA9dggBGAZNqgDO3n5CyJyHc1zPQYDT4vIKuC/QFN7rVOB540xNQBm/xPFTwTmGWOKjTGN2DnkTtxPfg7FbOfvKmCRMabSGFMM1DkjnU9xHsuxU0uNwl6XVcCpIvIXETnBGFOxj/R/JHbOweVAJs3X40DODfY92mxsNfLrwPEopboUnQ5JKXU4qpsWROQkbMB0jDGmxmnLFepsbjDGNDVQdeF89hhjbhCRo4GzgGwRGQ/cAhQC47AlSXVNp8CWaB0oaWdbm/k5RHucv26v5abnQU4+/myM+c9eGRQ5CjuVyp9F5BNjzO9bbR8M3AFMNMbsctqvhXrtsr9zw97XTBsKK9XFaMmYUupgVGKrwtoSDexyArFRwOT9JSYiQ40xi4wxvwFKsKVp0cBOY+cfvYLmErNPgGuaehqKSOx+8rQI+IGIxItIIHAJ8OWBvMhW2nvNB2IONt+RACKSKiKJTg/QGmPMK8BDwJFtnK8vNuCtcNq1nXEI558kIoOdtmI/Br45jNeilOoAWjKmlDpgxphSEZkvIquBWmwJVpOPgRtEZCWwHltVuT9/E5Hh2NKjz4EVwBPA/0TkImAuTumbMeZjp+RsqYjUAx8Cv8K24XpSRGqBY7zyulNE7nHSEOBDY8ysQ3jZM7HVprcCFx7swcaYT0RkNLBARACqgMuBYdjX7wYasJOKg21f9pGI7DTGnCwiy4EcYDO2WvdgLcB2vBgLfAW8cwhpKKU6kA5toZRSPZRTdXyHMeZsP2dFKdUOraZUSimllPIjLRlTSilARO4FLmq1+r/GmD/6Iz9Kqd5DgzGllFJKKT/SakqllFJKKT/SYEwppZRSyo80GFNKKaWU8iMNxpRSSiml/Oj/AXYZ8jBfzO0gAAAAAElFTkSuQmCC",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"day_order = [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
"\n",
"total_sales_by_weekday = (\n",
" sales\n",
" .resample('D') # resample by day\n",
" .sum() # compute total daily sales\n",
" .rename(lambda idx: idx.day_name()) # extract week day name from date-timestamp\n",
" .groupby('transaction_timestamp') # group by day of week\n",
" .quantile([.25, .5, .75]) # compute median and IQR of sales values\n",
" .unstack() # flatten output (results in a DataFrame)\n",
" .reindex(day_order) # force index to follow weekday order \n",
")\n",
"\n",
"total_sales_by_weekday.plot.line(title='Median and IQR of total sales by weekday', figsize=(10,4));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another common plot for Series is the bar plot. Let's look at the median values from the analysis above. If we peak at the result we see we have a Series that contains the median total sales values for each weekday."
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"transaction_timestamp\n",
"Monday 6764.620\n",
"Tuesday 6581.320\n",
"Wednesday 5987.355\n",
"Thursday 6244.785\n",
"Friday 6698.905\n",
"Saturday 8461.210\n",
"Sunday 9276.320\n",
"Name: 0.5, dtype: float64"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"median_sales_by_weekday = total_sales_by_weekday[0.50]\n",
"median_sales_by_weekday"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rather than plot this as a line chart as we did above, we can use `.plot.bar()` to create a bar plot:"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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FcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRXCSdvMzKwQTtpmZmaFcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRXCSdvMzKwQTtpmZmaFcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRXCSdvMzKwQTtpmZmaFcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRWiz0lb0iBJd0i6JI8vKekqSQ/k9yUq8x4t6UFJ90vasVK+kaSpedopkvTu7o6ZmVn7mp+a9meBeyvjRwFXR8RY4Oo8jqS1gH2AtYGdgJ9IGpSXORUYD4zNr53eUfRmZmYdpE9JW9IKwK7A6ZXi3YGz8vBZwB6V8vMj4tWIeBh4ENhE0vLAsIi4KSICOLuyjJmZmfWirzXtHwJfAv5VKVs2ImYC5Pdlcvlo4LHKfNNz2eg83LXczMzM+qDXpC3pQ8CTEXFbH9fZ7Dx19FDebJvjJU2WNHnWrFl93KyZmVl760tNe0tgN0nTgPOB90s6B3giN3mT35/M808HVqwsvwIwI5ev0KR8HhExISLGRcS4kSNHzsfumJmZta9ek3ZEHB0RK0TEGFIHsz9GxH7AJOCAPNsBwMV5eBKwj6SFJa1M6nB2a25Cny1ps9xr/OOVZczMzKwXg9/BsicCEyUdDDwK7AUQEXdLmgjcA7wBHBYRb+ZlDgXOBIYAl+WXmZmZ9cF8Je2IuBa4Ng//E9i+m/lOAE5oUj4ZWGd+gzQzMzPfEc3MzKwYTtpmZmaFcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRXCSdvMzKwQTtpmZmaFcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRXCSdvMzKwQTtpmZmaFcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRXCSdvMzKwQTtpmZmaFcNI2MzMrhJO2mZlZIZy0zczMCuGkbWZmVggnbTMzs0I4aZuZmRXCSdvMzKwQTtpmZmaFcNI2MzMrhJO2mZlZIXpN2pJWlHSNpHsl3S3ps7l8SUlXSXogvy9RWeZoSQ9Kul/SjpXyjSRNzdNOkaT+2S0zM7P205ea9hvAFyJiTWAz4DBJawFHAVdHxFjg6jxOnrYPsDawE/ATSYPyuk4FxgNj82und3FfzMzM2lqvSTsiZkbE7Xl4NnAvMBrYHTgrz3YWsEce3h04PyJejYiHgQeBTSQtDwyLiJsiIoCzK8uYmZlZL+brnLakMcAGwC3AshExE1JiB5bJs40GHqssNj2Xjc7DXcubbWe8pMmSJs+aNWt+QjQzM2tbfU7akoYCFwJHRMTzPc3apCx6KJ+3MGJCRIyLiHEjR47sa4hmZmZtrU9JW9KCpIR9bkT8Nhc/kZu8ye9P5vLpwIqVxVcAZuTyFZqUm5mZWR/0pfe4gJ8D90bE9yuTJgEH5OEDgIsr5ftIWljSyqQOZ7fmJvTZkjbL6/x4ZRkzMzPrxeA+zLMlsD8wVdKUXPYV4ERgoqSDgUeBvQAi4m5JE4F7SD3PD4uIN/NyhwJnAkOAy/LLzMzM+qDXpB0RN9D8fDTA9t0scwJwQpPyycA68xOgmZmZJb4jmpmZWSGctM3MzArhpG1mZlYIJ20zM7NCOGmbmZkVwknbzMysEE7aZmZmhXDSNjMzK4STtpmZWSGctM3MzArhpG1mZlYIJ20zM7NCOGmbmZkVwknbzMysEE7aZmZmhXDSNjMzK4STtpmZWSGctM3MzArhpG1mZlYIJ20zM7NCOGmbmZkVwknbzMysEE7aZmZmhXDSNjMzK4STtpmZWSGctM3MzArhpG1mZlYIJ20zM7NCOGmbmZkVwknbzMysEE7aZmZmhXDSNjMzK4STtpmZWSGctM3MzArhpG1mZlYIJ20zM7NCtDxpS9pJ0v2SHpR0VKu3b2ZmVqqWJm1Jg4AfAzsDawH7SlqrlTGYmZmVqtU17U2AByPioYh4DTgf2L3FMZiZmRVJEdG6jUkfBXaKiE/m8f2BTSPi013mGw+Mz6OrA/e3LMh5LQ08VeP269TJ+w7ef+9/5+5/J+871L//74mIkc0mDG5xIGpSNs+vhoiYAEzo/3B6J2lyRIyrO446dPK+g/ff+9+5+9/J+w4De/9b3Tw+HVixMr4CMKPFMZiZmRWp1Un7L8BYSStLWgjYB5jU4hjMzMyK1NLm8Yh4Q9KngSuAQcAvIuLuVsbwNgyIZvqadPK+g/ff+9+5OnnfYQDvf0s7opmZmdnb5zuimZmZFcJJ28zMrBBO2vYWSevUHYPVR9KSdcdgZj1z0m4i3261E50m6VZJn5I0ou5grOVukfQbSbtIanZPhbbXwZ/9jlfKsXfSbu5BSd/rtPuiR8RWwMdI19JPlvQrSR+oOayWkfRpSUvUHUeNViP1mt2f9Bn4lqTVao6p1Trysw8g6SRJa9cdR42KOPZO2s2tC/wNOF3SzZLGSxpWd1CtEBEPAMcAXwa2AU6RdJ+kj9QbWUssB/xF0sT8NLqOqm1GclVE7At8EjgAuFXSdZI2rzm8VunYzz5wHzBB0i2SDpE0vO6AWqyIY+9LvnohaWvgPGAEcAHwjYh4sNag+omkdYFPALsCVwE/j4jbJY0CboqI99QaYAvkRP1B0t9hHDCR9Hf4e62BtYCkpYD9SDXtJ4Cfk25+tD7wm4hYub7oWq+TPvtVklYn/f/vC9wI/Cwirqk3qtYayMfeNe0mJA2StJuki4CTgf8FVgF+D/yh1uD614+A24H1IuKwiLgdICJmkGrfbS/Sr9jH8+sNYAngAknfrTWw1rgJGAbsERG7RsRvI+KNiJgMnFZzbC3RwZ994K3zumvk11PAX4HPSzq/1sBaoJRj75p2E5IeAq4h1bD+3GXaKRFxeD2RWX+SdDipSfgp4HTgdxHxuqQFgAciYtVaA+xnkhQd/oXQyZ99Sd8HdgOuJu3/rZVp90fE6rUF1wKlHHsn7SYkDY2IF+qOo9UkjQW+DawFLNIoj4hVaguqhSR9nfSBfaTJtDUj4t4awmoZSSOBLwFrM/fxf39tQbVYp372ASQdBJwfES81mTY8Ip6rIayWKeXYO2k3IWkR4GDm/fI6qLagWkDSDcCxwA+AD5POaykijq01sBaTtAxzH/dHawynZSRdCfwaOBI4hNTqMCsivlxrYC3UqZ/9hnz1xFjm3vfr64uodUo59j6n3dwvST2JdwSuIz1CdHatEbXGkIi4mpSoH4mI44BOqmV9WNIDwMOk4z4NuKzWoFprqYj4OfB6RFyXv6w2qzuoFuvUzz6SPglcT3qg0/H5/bg6Y2qxIo69k3Zz742IrwIvRsRZpN7U76s5plZ4pXH+Nl+z/O/AMnUH1ULfJCWpv+We0tuTes92itfz+0xJu0ragPTF1Uk69bMP8FlgY+CRiNgO2ACYVW9ILVXEsXfSbq7x5fVsvrXncGBMfeG0zBHAosDhwEakS38OqDOgFns9Iv4JLCBpgXyZy/o1x9RK38zX5n6B1ER+OvC5ekNquU797AO8EhGvAEhaOCLuA9q681kXRRz7lj5PuyAT8rmdr5KuUx0KfK3ekPpfRPwlD75AOp/daZ6VNJTURHiupCdJl311hIi4JA8+B2xXZyw16sjPfjY93774d8BVkp4BZtQaUWsVcezdEc2Q9Hug23+EiNitheHURtJiwCuASLdzHQ6cm2vfbUvS/9Hz8R8Ql7pY60jahvT/f3lEvFZ3PDaHa9oVkj7f0/SI+H6rYmmxk/L7R0gdMc7J4/uSOmN1hIh4sTJ6Vm2BtN7k/L4l6XK/X+fxvYDbaomoxTr4s9/d092m5vehwNMtDKflSjv2TtpzWzy/r07qkDEpj3+Y1GTaliLiOgBJ34iIrSuTfi+pbfe7QdJseq5pDrj7D7+bcqcbJB0IbBcRr+fx04ArawytlTrys5/dRvr/F7AS8EweHgE8CrT77WuLOvZO2hURcTy8db3qhhExO48fB/ymxtBaZaSkVSLiIQBJKwMja46p30XE4vDWzVUeJ1360WgiX7yHRdvNKNL+NmpWQ3NZ2+vkz37jnvL5R9qkiPhDHt8Z2KHO2FqhtGPvpN3cSkD1PM5rDMBehP3gc8C1+XZ+kPb5v+sLp+V2jIhNK+OnSroF6IT7jgOcCNwhqfFwiG3orOt0oXM/+wAbR8QhjZGIuEzSN+oMqMWKOPZO2s39kvRIwotIzUb/Dpxdb0j9LyIuz7cyXSMX3RcRr9YZU4u9KeljwPmk474v8Ga9IbVORJwh6TKg8cPlqIh4vM6YatCRn/3sKUnHkPq0BOmJb23dCbOLIo69e493Q9JGwFZ59PqIuKPOeFpB0l6k3qKz84d3Q+Cbjad9tTtJY0hP99mS9KG9ETgiIqbVGFbLSNoSmBIRL0raj3T8T252L/Z2JmlD4N/yaEd89uGtDmnHAluT/v+vB74eEW3dEa2qhO99J+1u5EfULUulNaLd70Et6c6IWFfSVqQHh5wEfKVLk7G1KUl3AusB65JqGL8APhIR29QaWAtIGhYRz3fTk5p2T1z5++6siNiv7ljqVML3vu+I1oSkzwBPAFcBlwCX5vd212gK3hU4NSIuBhaqMZ6WkvRdScMkLSjpaklP5Rpnp3gjP5pzd+CUiDiZzumI96v8fhvpErjGqzHe1iLiTVJH1I75vHdVyve+a9pNSHoQ2LTdb6rRlaRLgH+QeoxuBLwM3BoR69UaWItImhIR6+d7ru9B6ph3TQft/3XA5aS74W1Nuu/0lIgYcPdf7g+SBKw40GpWrSLpp6RTIpOAt+5ZMNCuU+4vpXzvu6bd3GOkWzl2mv8gPdlnp4h4FlgS+GKtEbXWgvl9F+C8dm8SbWJv4FXg4NwBbTTwvXpDap3cynBR3XHUaAapZrkAqYWl8eoURXzvu/d4cw+RLn26lPQlBrT/L86IeCnfb3sr4AHSfbcfqDeqlvq9pPtILQyfkjSSdFvTtpfP5Z0TEW9dl5trnAOu92w/u1nSxpX78HeMxvXKHayI730n7eYeza+F6KxzuscC40h3BjqDVPM8h9Sbuu1FxFGSvgM8HxFvSnqJdH637TX2V9LwiBjwtY1+tB1wiKRppCZikSrh69YaVQvk6/PnOV8aEe+vIZw6FPG973PaPZC0OOkD+0LdsbSCpCmkZ+jeHhEb5LI7O+ELC0DSosDngZUiYny+Zn31ytOv2pqkiaTniV/F3Oc02/6BIZJWiohHJb2n2fROuOwtX+7UsAiwJ6lz4pdqCsmacE27ifws1V+Szuki6Sng4xFxd62B9b/XIiIkBbz11KtOcgapt/AWeXw66TaGHZG0Sb1lL607iJr8jnQLy0ckXRgRe9YdUKtFRNeHw9yYOyd2hFJaGpy0m5sAfD4irgGQtC3wM+Z8mberibkH6QhJ/wUcRNrvTrFqROwtaV+AiHg59yjuCI0Hh3So6nFepbYoatTlGvUFSFeQLFdTOHU4sjL8VktDTbF0y0m7ucUaCRsgIq7thFpnRJwk6QPA86Tz2l+LiKtqDquVXpM0hPxrW9KqVDqktDtJD9O8ptEJSSy6Ge4k1ad9vQE8DBxca0QtVEpLg5N2cw9J+iqpiRzSPXgfrjGelslJupMSddWxpOuUV5R0LqkD3oG1RtRa4yrDi5Cep930DmFtaD1Jz5MS1pA8DHM6orX141mzNSNirqslJC1cVzCt1qSlYRwDsKXBHdGakLQEcDzp0ieR7sF7XEQ8U2tg/azLc6UXIvUef7FDvrAAkLQUqTOWgJsj4qmaQ6qVpBsiYqve57TSSbo9IjbsraxddWlpegOYRrr3+g21BdWEa9pN5OTc9j1mu2o8V7pB0h7AJvVEU5tFgGdIn421JBER19ccU0vkB2U0NGoanXRzjY4kaTnSjXSGSNqAOef3hwGL1hZYi0jaGHis8lzxA0jns6cB99QYWlOuaVdImtTT9IjYrVWxtJKkwRHRtMOFpJsjYrNWx1SHfI323sDdwL9ycbTrce+q8hxtmFPTOCki7q8nImuFnKQOJP1Iq95nfTZwZkT8to64WkXS7cAOEfG0pK1Jj+b9DLA+6ZTBR+uMrysn7QpJs0i3sjsPuIW5e5QSEQOuU8K7odEEJukjleJGTWubiNi8ptBaStL9wLod9gxxMwAk7RkRF9YdR6tJ+mvj+QKSfgzMiojj8viUiFi/xvDm4ebxuS0HfADYF/hP0jWr53XA9dkNH2beczodUcvMHiKdx+/IpJ07He0JjGHuRxN+va6YrHUi4kJJuwJrk04TNcrb/fgPqrQ2bg+Mr0wbcDlywAVUp/x4usuBy/MX2L6ke9F+PSL+r97o+tUykj4P3NWlPID9gQF1791+9BIwRdLVzH3v4U7p33Ax6YEJt9GhP1w6maTTSOewtwNOBz4K3FprUK1xHnBdvonWy8CfACS9lwH4ABEn7S5yst6VlLDHAKcAbX1OBxgEDKXL6YAONCm/OtUKEbFT3UFYbbaIiHXzrYuPl/S/tP93HxFxQv6hvjxwZcw5Z7wA6dz2gOKkXSHpLGAd4DLg+IjoWvNsVzM7oAmsVx1+RzCAP0t6X0RMrTsQq8XL+f0lSaOAp4GVa4ynZSLi5iZlf6sjlt44ac9tf9KDElYDDq/cwbLdb7DQ0TVsSVPp4S5Y7f7AFEl3kXrLDwY+IekhUvN4xzzhygC4RNII4LukUySQmsltAHHSroiIBeqOoSbb1x1AzT6U3w/L74074X2MdJ673Y0mXd5iHahynfI38vhQYCpwH/CDOmOzefmSL7NM0o0RsWVvZe2mk+56ZfMq7TrlTueattkci0naqnHbQklbAG3/oBjmXD3QVER0ytUDnWpQRDydh/cGJuTrtS+UNKW+sKwZJ22zOQ4GfiFpOOkc93Okx5O2O1890NmKuk650/mAmGX50XzrSRpGOnU04K7R7Ce+eqCzFXWdcqfzOW2zTNKywLeAURGxs6S1gM0j4uc1h9avJN0RERvUHYfVR9JmzLlO+cVcthowNCJurzU4m4uTtlkm6TLgDOB/ImI9SYOBOyLifTWH1q8kLVk5p2lmA1inXuJk1szSETGR/ISvfI7vzXpD6n9O2GblcNI2m+NFSUuRb7SSmwx9Ts/MBgw3j1vHk3QEcCOp9/T3SbeyvRsYCewVEX+tLzozszmctK3jSToJ2AJYg3QXqH8A1wK/joinagzNzGwuTtpmmaSFgHGkBL55fj0bEWvVGpiZWebrtM3mGAIMA4bn1wzSPZjNzAYE17St40maAKwNzAZuAW4Gbo6IZ2oNzMysC/ceN4OVgIWBx0nns6cDz9YZkJlZM65pmwFKD09fm3Q+ewtSD/KngZsi4tg6YzMza3DSNquQtAKwJSlxfwhYKiJG1BqUmVnmpG0dT9LhpCS9JfA66Zrtm/L71Ij4V43hmZm9xb3HzWAMcAHwuYiYWXMsZmbdck3bzMysEO49bmZmVggnbTMzs0I4aZu9DZJGSPpU3XFUSTpQ0qjK+OmS3rVbsErao7o+SV+XtMO7tf4m2ztC0qL9tX6zEvmcttnbIGkMcElErNOlfFBE1PIMbknXAkdGxOR+Wv+ZpH2+oD/W32R704BxfmiL2RyuaZu9PScCq0qaIukvkq6R9Cvyvcol/U7SbZLuljS+sZCkFySdIOmvkm6WtGwu30vSXbn8+lw2RtKfJN2eX1tU1vMlSVPz/CdK+ijpYSfn5piGSLpW0rg8/755/rskfae3eLrK294N+F5e/6qSzszbRdI0Sd+SdJOkyZI2lHSFpL9LOqSyni/mv9edko7PZYtJujTHcJekvfNleKOAayRdk+c7Na/77sayfd22pG0lXS/pIkn3SDpNkr//rDwR4Zdffs3ni3SZ2F15eFvgRWDlyvQl8/sQ4C7STVoAAvhwHv4ucEwengqMzsMj8vuiwCJ5eCwwOQ/vDPwZWLTLtq4l1UypjpOS36Ok54MPBv4I7NFTPN3s85nAR5uNA9OAQ/PwD4A7gcXzNp/M5R8EJpCeW74AcAmwNbAn8LPKeodX1rl0k7/poLxv687HtrcFXgFWyctfVd0Xv/wq5eVfmmbvjlsj4uHK+OGS/kp6+MiKpKQL8BopWQHcRkr+kG7kcqak/yIlFYAFgZ9Jmgr8BmicT94BOCMiXgKIiKd7iW1j4NqImBURbwDnkpJlT/G8HZPy+1TgloiYHRGzgFckjSAl7Q8CdwC3k55fPjbPv4Ok70j6t4h4rpv1/4ek2/PyazPn79GXbUM6Rg9FOn1xHrDVO9hXs1r45ipm744XGwOStiUl1s0j4qV8rnmRPPn1iGh0JHmT/BmMiEMkbQrsCkyRtD7wGeAJYD1SzfSVxiZINeS+Ug/TmsbzNr2a3/9VGW6MD85xfDsifjpPgNJGwC7AtyVdGRFf7zJ9ZeBIYOOIeCafX1+kMktv24Z5/2bu0GPFcU3b7O2ZTWqCbWY48ExO2GsAm/W2MkmrRsQtEfE14ClS7Xw4MDPSbVT3Z04N/ErgoEbPaklL9hLTLcA2kpaWNAjYF7iuLzvZRU/73BdXkOIeCiBptKRlco/3lyLiHOAkYMMm2xtG+mH0XD7vvvPb2P4mklbO57L3Bm54B/tiVgvXtM3ehoj4p6QbJd0FvEyqETdcDhwi6U7gflITeW++J2ksqTZ6NfBX4CfAhZL2Aq4h1+Yj4vJcE58s6TXgD8BXSOeYT5P0MrB5JdaZko7O6xDwh4i4+G3s9vmk5vrDgY/O78IRcaWkNYGbJAG8AOwHvJe0//8i3fv90LzIBOAySTMjYjtJdwB3Aw+RTifMr5tIHQjfB1wPXPQ21mFWK1/yZWZtL5+yODIiPlRzKGbviJvHzczMCuGatpnNRdL/AHt1Kf5NRJxQRzxmNoeTtpmZWSHcPG5mZlYIJ20zM7NCOGmbmZkVwknbzMysEE7aZmZmhfj/wkNMUqHbpR0AAAAASUVORK5CYII=",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"median_sales_by_weekday.plot.bar(title='Median total sales by weekday', figsize=(8,4));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A common pattern you'll use is to follow a `.value_counts()` method call with a bar plot. For example, say we want to assess the number of transactions in our data by department. We could easily get this with the following:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tuEaD45uZWRvp0leCBcWrwG0kjQFeIz0TfC23N5wpKOkCSaMlPVz3wNINkh6XdH3ZpmtJt0H3Ba6psf8ASSMljZz+wTv1DmdmZi3Q5YtgzgacDryRm4ZHxMbARsCPJPXO7eW5gACLkT7YMg7oU2qMiMNJ6fSVUm7L++4JHETKGaTQ/hApeX6ZiHi62vydJ2hm1n66dBGUtCxwIXB+lMVl5MJzCnBMbroX+KakxfK+3wJGR8R04G5gEUk/KgyxaJXDXg18SdI3G+h7HCmayczM5oKu+Eywm6RRzPyKxJXAWVX6XkgK110jIsZIOh+4T1KQrhwPBoiIkLQHMETS0cBk4H1mFtAZImKapN2AsySdDbxOyhs8qULf2+fkRM3MbM44T7ATcZ6gmVnLOU/QzMysAhdBMzNrWi6CZmbWtFwEzcysabkImplZ03IRNDOzptUVvyfYZbV1lJIjjMys2c1zV4KSQtKVhfcLSJos6db8/qD8flThZ4NC/yMlfShpCUlLF/q8JunlwvuFCvtsK+kdSY9JelLSbwrbNpd0r6Txkp6S9GdJi+Z5nJ/7zCfpckmXKOku6Y+SJuQxH5F0SO7bQ9K0wrEeknRgR/xuzcxsVvPileD7wIaSuuW4ox2Bl8v6DI2II6rs3x94GNgzxxf1hsqhuWWGR8Rukj4HjMpF9yXg78C+ETEixzHtRVpTlDyuSCvPLAh8L68u82dScO/aOcx3WeD7hWNNyJmGpbVNr5c0X0RcWve3Y2ZmbWaeuxLMbgdK9+r6UyNloUhST6A7cAKzhtY2LCLeBx4BegKHA5dHxIi8LSLiuoh4vbDLOcDSwHdzwesJbA6cEBGf5f0mR8RpVY73HPBz4Cetma+ZmbXevFoErwX2zSntGwMPlm3vV3Y7tFtuLxXM4cC6kpajhSQtDWxJSoPYkFQQq9kP2Ix0pfhpbutFWnj7sxYc9lFgvSrzcZSSmVk7mSeLYESMAXqQitptFboMjYjehZ9puX1f4NpcgK4Hvt2Cw24j6THgTuDUiBjXwD6PAquTrvwqknR8LtSv1BhH1TY4SsnMrP3Mk0UwuxkYTOO3QjcmJcX/W9IkUkGseEtU0p6Fq8jSoqrDI2LTiNgsIi7MbeNIV3rVPAXsAwyV1Cu3PQFsktPsiYiTI6I3sHiNcTYFnqx7kmZm1qbm5SJ4CXBiRIxtsH9/YFBE9Mg/KwErS1q9vGNE3FC4iqwVy3A+cKCkLUoNkg6QtEJhrAeAQ4F/SlotIp4FRgInSZo/77MIVa72JPUgFfvzGjxPMzNrI/Pip0MBiIiXSB86qaSfpK0L7w8jXfntWtbvhtxe8UMpDczhdUn7AoPz88XPSOG715f1uzV/AvRfkrYh5RCeATwr6S1gGrNmD/bMt14XIWUNntfIJ0M3WnkJRvq7fWZmbcZ5gp2I8wTNzFrOeYJmZmYVuAiamVnTchE0M7Om5SJoZmZNy0XQzMyalougmZk1rXn2e4LzGkkB/DUivpPfLwC8CjyY0ycOIn03sJh4sV9EPJH7HwmcAixP+r3flfusAEwHJuf3m0fEx5Xm0BZ5gs4QNDObyUWwcXMr4snMzNqJb4e2zFyLeDIzs7bnItgycy3iyczM2p6LYAvMjYgn5wmambUfPxNsuVLE07akRPmayiKeABYCngMuaORgEXERcBHAwiuu7YVezczakK8EW67dIp7MzKxjuQi2UES8FBG1Ip6KzwS3It0KvaGsXyniyczM5iJHKXUijlIyM2s5RymZmZlV4CJoZmZNy0XQzMyalougmZk1LRdBMzNrWi6CZmbWtLxiTCfSmiglRyeZmVXnK8E5IGl6/lL8OEmjJf1c0nx527aS3in78vwOedvUCmMNkjSwo8/BzKyZ+UpwzkyLiN4AORniamAJ4Dd5+/CI2G0uzc3MzOrwlWAbiYg3gAHAEcorZZuZ2bzNV4JtKCKey7dDS3mB20gaVeiyV0RMaMmYkgaQiivzL75sm8zTzMwSF8G2V7wKnOPboY5SMjNrP74d2oYkrQlMB96Y23MxM7P6XATbiKRlgQuB88PRHGZmnYJvh86ZbvmZ34LAp8CVwFmF7eXPBE+KiOuARSW9VGgv7lPVRisvwUh/78/MrM24CM6BiJi/xrZhpK9LVNrmK3Azs3mA/zE2M7Om5SJoZmZNy0XQzMyalougmZk1LRdBMzNrWi6CZmbWtJr+KxKSpgNjSb+LJ4EDI+IDSQsArwEXR8Rxhf67Ab8j/R+IBYFzgGWAb+cuG+XxAC4BlgIOASYDC+V97wIeBL4YEa/lcf8AvBARp1aba708QWcHmpm1jK8EcxxSRGwIfAwcmtt3AsYD+5RSISQtSFrH8xsRsQmwKTAsIk7OY/QujNc7Is7NYw3J23YH/gT8DzgNGJzH7QNsDZzZ/qdrZmYlLoKzGg6slV/3J13lvQBsmdsWI10xvgkQER9FxPhGB4+IZ4APgCVJxbSnpO2A84EjIuKTtjgJMzNrjItglm9/7gqMldQN2B64FbiGVBCJiLeAm4HnJV0jaf9SknyDx+gDPBMRb0TEZ8CPgH8AT0fEvW17RmZmVo+L4Mz1P0eSrvr+AuwG3BMRH5CK1J6S5geIiINJBfIhYCDpuV89R0oaT3oOOKjUGBGjgMeBP1TbUdIASSMljZz+wTstPjkzM6uu6T8YQ36GV2yQ1B/4kqRJuWlpYDvgPwARMZZ0xXglMBE4qM4xhkTEYEnfAq6Q1DMiPszbPss/FTlP0Mys/fhKsIykxUkfUlktInpERA/gcKC/pO6Sti107w083+jYEXE96YrzwLaar5mZtZ6L4Oy+BdwdER8V2m4CvgnMDxwtaXy+hfpb6l8FljsR+HlLniWamVn7kPNfO4++ffvGyJEj5/Y0zMw6FUmPRETfStt8NWJmZk3LRdDMzJqWi6CZmTUtF0EzM2taLoJmZta0XATNzKxpuQiamVnT6rTLpkkK4KyI+EV+PxDoHhGDJF0G3BoR1xX6TwW+CFyZm1YD3sk/U4CDSXmC40m5fyOBH0TEJ3mVmIGkdUR/mvffIPedDvwLeAroGxFH5OMNAH6e+74L/Dwi7svbhuW59s3v+wKDI2LbWudcK0/QWYJmZi3Xma8EPwK+JWmZRneIiLGF3L+bgaPy+x1ylwl520bAKsA+ZftfWtj/FWC7/P7YYr8cvPtDYOuIWI+UUXi1pBUK3ZaTtGsLztfMzNpYZy6Cn5IWlj6yrQeOiOmklIiVWznEMaQCOyWP9yhwOWkN0pIzgBPmZJ5mZjZnOnMRBLgA2F/SEm05qKRFgC1ItzlboxfwSFnbyNxeMgL4KIfq1pqLo5TMzNpJpy6CEfEucAXwk/JNlbo3MGTPvDD2m8ALETFmzmY4C1WYw0nUuRqMiIsiom9E9J1/0Tat9WZmTa9TF8HsbOAHwOcKbW8CS5beSFqK9OGXekrPBNcCtpT0zVbO6Qlgs7K2Prl9hoi4G1gE2LKVxzEzsznQ6YtgRLwF/I1UCEuGAf0kLZTfHwTc04IxXwWOBY5r5bROB06TtDSApN55DpUS5E8Gjm7lcczMbA502q9IlDkTOKL0JiJulbQZ8Iik6cAE0ic0W+JGYJCkbVo6mYi4WdLKwAP5qxzvAQfk4lre9zZJkxsZd6OVl2CkvwphZtZmnCfYiThP0Mys5ZwnaGZmVoGLoJmZNS0XQTMza1ougmZm1rRcBM3MrGm5CJqZWdPqKt8T7DD5e4djC017ANtSiFHK/YYBAyNipKRJpO8KTs+b742In1SKfKqlUpSSI5TMzFrPRbDlpuWl1WaQ1Mh+25VSJczMbN7g26FmZta0fCXYct1y0gTAxIjYs8H97sm3UgEuj4ghbT81MzNrCRfBlpvtdijVY5qK7a26HSppADAAYP7Fl23p7mZmVoNvh7aNWaKbskbjm2pynqCZWftxEWwbDwNfkrQCgKS+wMLAi3N1VmZmVpNvh7aBiHhd0k+B2yTNB0wF+kfEZ4VuxWeCYyLiu/n1nySdnV+/GBFf7JhZm5mZo5Q6EUcpmZm1nKOUzMzMKnARNDOzpuUiaGZmTctF0MzMmpaLoJmZNS0XQTMza1ougmZm1rTmqAhKOl7SOEljJI2StIWkYZLG5/ejJF1Xts9oSdeUtV0maWJhn5/k9iUkXSFpQv65QtIShf16Sbpb0tOSnpH0K+VcI0kHSZos6bG87Q5JW9U4l+n52I9L+rukRXP7CpKuzcd/QtJtktaR1EPS44X9D5H0qKQl8/ufS3pK0th8zmdJWjBvm5Tbx+YxT5K0cL3fd6U8QTMza71WF0FJXwR2A/pExMbADsxcJmz/iOidf/Yu7LN+PuaXJX2ubMijCvucm9v+AjwXET0joicwEfhzHqsbcDNwakSsA2wCbAUcVhhzaERsGhFrA6cC1+c5VDItH3tD4GPg0FxQbwCG5TlsAPwSWL7sd/Ed4MfAThHxP0mHAjsBW0bERsAXgDeAboXdtsvbNgfWBC6qMi8zM2snc3IluCIwJSI+AoiIKRHxSp199gOuBO4Evlmro6S1gM2A3xWaTwT6SuqZx7o/Iu7Mx/8AOAI4ttJ4EXEPqdAMqDNHgOHAWsB2wCcRcWFhnFERMbwwz33yMXcqpEQcD/woIt7O+3wcEadGxLsV5jUVOBTYQ9JSDczNzMzayJwUwTuBVfOtyD9I+kph21WFW5tnFNr7AUOBa4D+ZeOdUdhnI2ADYFRElNbbJL8eBfTKP48UB4iICUB3SYtXmfOjwHq1TkrSAsCuwFhgw/JjlFkdOJ9UAF/L+y8GdI+IibWOUzbvd0lXuWtXmM8ASSMljZz+wTuNDmlmZg1odRHMVzCbka6sJgNDJR2UNxdvhx4FIOkLwOSIeB64C+hTen6WFW+HjgVE5Zy+Unu17dRoV41TKoXljgReIN2KrWdy7rtPhfmlN9LOubBPqvVMstrcHKVkZtZ+5ihFIl+ZDQOGSRoLHFije39gPUmT8vvFgb3Iz/gqGAdsKmm+UhpDTmjYBHiS9Fzuy8UdJK0JTI2I9/LnY8ptCjwpaVXgltx2Yb7dOVtYrqRxwN5U9wHpqvE+SW9ExFUR8a6k9yWtERETI+IO4A5JtwILVRokXz32AJ6ucSwzM2tjc/LBmHUlFW/f9Qaer9J3PuDbwMYR0SMiegC7M/st0Rki4lngMeCEQvMJwKN521XA1pJ2yMfoBpwLnF5lDl8hXbVeHBEvFq46L6zUP7sbWFjSIYVxvlC89RsRk4FdgN9L2jk3nwL8UdLn8z4CFqkyr+7AH4AbI+J/NeZiZmZtbE6uBLsD5+V/6D8FniUVmetIzwSn5X5TgJOAlyPi5cL+9wIbSFqxxjF+kI/xLOl24YjcRkRMk7R73n4BMD/pQzfnF/bvJ2lrYFHSM7e9IuLJRk8wIkLSnsDZko4FPgQmAT8r6zdR0jdJeYLfAv6Yj/mgpI9I+YL3k4p6yT25OM5H+gRq8QNAFW208hKMPPXrjU7fzMzqcJ5gJ+I8QTOzlpPzBM3MzGbnImhmZk3LRdDMzJqWi6CZmTUtF0EzM2taLoJmZta0Ol0RVIX4psK2BSRNkXRK2T7DJFX8eKykPSWFpPUKbT0kTcsxTE9KekhSxdVwJG0r6Z08lzGS/iNpubI+N0kaUdY2SNLLeb8nJFVdOKDEUUpmZm2rUxXBOvFNkOKLxgP7qMq6aRX0B+4D9i1rn5BjmNbP246U9L0qYwzPq89sDDwMHF6Y8+eBPsDnJa1Rtt+QvFTb7sCfSnmDZmbWMTpVEaR+fFN/4BzSotZb1hssL1n2JdIqNOVFcIaIeA74OfCTOuMJWAwoLn+2F2md0murHSMiniGtQ7pkpe1mZtY+OlsRrBrflNcO3R64lcpRTZXsAfwrIp4G3pLUp0bfWjFM2+QEihdIV6eXFLb1z/OpOqd83Gci4o0G5mxmZm2kUxXBOvFNuwH35HDdfwB7Spq/zpD9SVdo5D9rFc5at1dLt0NXBS4lL+ItaXlSOO99udB+KmnDwn5HShoPPAgMqnhQ5wmambWbTlUEIcU3RcSwiPgNKUl+r7ypP7BDjmp6BFialAxfkaSlga8Cf877HEVacLtasduUFOFUz83MjHjqR7rFOTEfowez3hIdEhHr5n5XSJotacJ5gmZm7adTFcFq8U1KSfJbA6sVopoOp/aV3d7AFRGxet5nVVLSxNYVjtsDGAyc18A0twYm5Nf9gV0Kc9qMCs8FI+J6UphvrTxGMzNrY3MUqjsXVItv+hZwd+kDM9lNwOmSFs7v/ynpk/x6BLAscGrZ+P8A9gNOA3pKeoyUA/gecF5EXFplXqVnggLeAQ7OhXM14L+lTjly6d3i1zoKTgSulnRxKUTYzMzal6OUOhFHKZmZtZyjlMzMzCpwETQzs6blImhmZk3LRdDMzJqWi6CZmTUtF0EzM2taLoJmZta02qQI5jy+KwvvF5A0WdKt+f1Bks7Pr9fN+X6jclbfRYX9Npd0r6Txkp6S9GdJi+b9J+d9Sj8b5Ny/xyvM5zJJe1eZ66Z5vjtXOIczC+8HShqUXxez/0o/n8/bts55g0/lnwG15iFpav5zPknnSnpc0lhJD1eIWpqF8wTNzNpWW60Y8z6woaRuETEN2BF4uUrfc0lrZt4EIGmj/OfywN+BfSNiRF7Dcy9SNBHA0Ig4ojhQXpWlpUr5gf2BOwrtHwHfknRKREypsN+QiBhcdvwVgKuBPSLiUUnLAHdIejki6lWrfsBKwMYR8ZmkVUi/RzMz6yBteTv0duDr+XUpPqiSFYGXSm8iYmx+eThweUSMyO0REddFxOttNcFcWPcGDgJ2Kluw+lPgIuDIFgx5OHBZRDwKKd8QOBo4toF9VwReLS2RFhEvRcT/6uxjZmZtqC2L4LXAvrmwbEyKB6pkCHC3pNslHVm6rQhsSEp/qKZf2e3Ibq2Y45eAiRExARgGfK1s+wXA/pIqxTUcWTj2PbmtV4U5j8zt9fwN+EYe70xJmzZ8FmZm1ibarAhGxBhSVFB/4LYa/S4F1ifd+twW+G9hketahubMvtLPtFZMs2Z+YES8C1xB5QT5IYVjlyKaBFRafDXK/pxtW0S8BKwLHAd8Btwlafvyzs4TNDNrP2396dCbSZFD1W6FAhARr0TEJRGxO+k25IbAOFLUULvIAbt7Ab/O2X7nAbtKWqys69nAD4DPNTDsOKB8UdbNgCfy6zdJeYKlOSwFzHjeGBEfRcTtEXEU8HtS0v0snCdoZtZ+2roIXgKcWHjONxtJu0haML9egRR++zJwPnBgMWZI0gG5T1vYARgdEavmfL/VSdFJexQ7RcRbpFuVP2hgzAuAgyT1zvNdmhTDdHrePox0G3eh/P4g4J7ct4+klfLr+Ui3kJ9v5bmZmVkrtGmeYL7Fd06dbjsB50j6ML8/KiJeA5C0LzBY0nKkW4T3Atfnfv0kFQNvDwNeAdaV9FKhvfTBlj9JOju/fhEYD9xQNpd/AD8CrixrP5OUWl90pKQDCu/3iIhJue3ifEUp4OyIuAUgIm6VtBnwiKTppLDdQ/P+y+X9SreCHyL9H4GqNlp5CUae+vVaXczMrAWcJ9iJOE/QzKzlnCdoZmZWgYugmZk1LRdBMzNrWi6CZmbWtFwEzcysabkImplZ0+p0RVDSCpKulTRB0hOSbpO0jqReku6W9LSkZyT9Ki+YjaTlJd0qaXRpn9zeI0co/a4w/jKSPtHM6KdKMUrFdUynKkU/jZJ0haRtlSOk8v675mXPnsxRS4Nze9VIqWocpWRm1rba9Mvy7S0XtRtIaRP75rbewPLAZcCPIuJOSYuSvgh/GGlVlxOBf0fEOXmfjQvDPgfsBvwqv/82aTm0otlilICheaxhwMCIGJnfb1uY74akL8B/PSKekrQAUMobrBgpZWZmHaezXQluB3wSEReWGiJiFLAOcH9E3JnbPiCt+FKKNCqPbxpTGHMa8KSk0hcp+5GWTWsLRwMnR8RT+bifRsQfqsyp6lJzZmbWPjpbEawWtzRbpFGOS+ouaXHS1eBfJN0j6fjSmp0FpRioVYDppOXYiirFKM3JfKF6pJSZmXWQzlYEq6kWaQQpn/cOYE3gYmA94DFJyxb6/AvYkRStNLTCGJVilOZIo5FSjlIyM2s/na0IVotbmi3SSNKawNSIeA9SOkREXB0R3wEeBr5c6hsRH5Ou2H5BepbY3vMtHbdSpFR5H0cpmZm1k85WBO8GFpZ0SKlB0heAZ4CtJe2Q27qRPnhyen7/1fxhGXLaQ0/ghbKxzwSOiYg323C+ZwC/lLROPvZ8kn6eX1eLlDIzsw7SqYpgpMiLPYEd81ckxgGDSM/wdgdOkDQeGEu62itFE20GjJQ0BhgB/DkiHi4be1xEXF7l0MVngqMk9WhwvmOAnwHXSHoSeJz0gRhIkVKPSxoN3EEhUsrMzDqGo5Q6EUcpmZm1nKOUzMzMKnARNDOzpuUiaGZmTctF0MzMmpaLoJmZNS0XQTMza1ougmZm1rSavghKml7+RfjyTMBC31qZhQdJmpzHeErSkWX7bpqzC3cua5/a6FzHvuy1Q83M2lLTF0FgWmFx7N4RMalSp7wU283AqRGxDrAJsBUps7BkaET0Br4EHC9p1cK2/sB9+U8zM5sHuAg2bj9qZxbOkNcffZa8RFq+WtwbOAjYSdIiHTRnMzOrwUUQuhVuhd5Qo1+9zMIZJK0GLAKUwnu/BEzM/YcBX2uryZuZWestMLcnMA+Ylm9h1lMzszD/2U/SdsC6wCER8WFu708K7iX/+R3g+kYmJ2kAMABg/sWXrdPbzMxawleCjaubWUh6JtgL2AY4U9IKkuYH9gJ+LWkScB6wa450qst5gmZm7cdFsHFXUSOzsCgiRgBXAj8FdgBGR8SqEdEjIlYnBffu0VETNzOzylwEq9te0kulH6A3tTMLy50GfI90K7P8WeM/SB+0AVi0eJxS6G4lG63sK0Ezs7bkPMFOxHmCZmYt5zxBMzOzClwEzcysabkImplZ03IRNDOzpuUiaGZmTctF0MzMmlaXLIKSjpc0TtKYvCboFpKGSRpfWCf0urJ9Rku6pqztMkkTC/v8JLcvIekKSRPyzxWSlijs10jk0mN52x2StmrkvBylZGbWtrpcEZT0RWA3oE9EbExaseXFvHn/QmTS3oV91if9Lr4s6XNlQx5V2Ofc3PYX4LmI6BkRPYGJwJ/zWI1GLm0aEWsDpwLX5zmYmVkH6nJFkBRfNCUiPgKIiCkR8UqdffYjLXN2J/DNWh0lrQVsBvyu0Hwi0FdST1oQuZS33wNcRF4k28zMOk5XLIJ3AqvmW5F/kPSVwrarCrc2zyi09wOGAtcwe+jtGYV9NgI2AEZFxPRSh/x6FCluqeHIpYJHgfVafKZmZjZHulyUUkRMlbQZKclhO2CopNJV2P4RMcu6Y5K+AEyOiOfzGqGXSFoyIv6XuxwVEdcV+q9J5UilUtRSI5FLlfatvMFRSmZm7abLFUGYcWU2DBgmaSxwYI3u/YH1cswRwOKk6KM/V+k/DthU0nwR8RmApPlIz/6eBJYHvlzcoRi5lD8fU27TvG+lc7mIdLuUhVdc2wu9mpm1oS53O1TSupLWLjT1Bp6v0nc+4NvAxjnmqAcpKaL8lugMEfEs8BhwQqH5BODRvK3hyKW8/SukK72LGzk/MzNrO13xSrA7cJ6kzwOfAs+Sisx1pGeC03K/KcBJwMsR8XJh/3uBDSStWOMYP8jHeJZ0K3NEbiMipknaPW+/AJif9KGbYuRSP0lbA4uSPlm6V0RUvBI0M7P24yilTsRRSmZmLecoJTMzswpcBM3MrGm5CJqZWdNyETQzs6blImhmZk3LRdDMzJqWi6CZmTUtF0Hq5g+OlnS/pHVz32GS+ubXkySNzfv9n6TVC2NOlbRRYfHttwrZhP+RNJ+kcyU9nsd4WNIac+t3YGbWjLriijEtUpY/+JGkZYCF8ub9I2JkXsT6DCrHLG0XEVMk/Za0fNohpQ0RMZa0bBuSLgNuLS3GLak/sBJpybbPJK0CvN8e52hmZpX5SrCx/MF7gbXqjDMCWLmFx321tAh3RLxUSK4wM7MO4CJYO3+w5BvA2Drj7ALc2ILj/g34Rr49eqakTVuwr5mZtYGmL4IRMZWUFD8AmEzKHzwob75K0ijgS8DAKkPcI+kNYAfg6hYc9yVgXeA44DPgLknbl/eTNEDSSEkjJ0+e3OjwZmbWgKZ/Jgg18wdnC+GtYDvSs7zLgBOBn7fguB8BtwO3S3od2AO4q6zPjDzBvn37erVzM7M21PRXgi3JH6wmIqYBPwO+K2mpBo/bR9JK+fV8wMYtPa6Zmc2Zpi+CpPzByyU9IWkMsAEwqKWDRMSrwDXA4Q3ushxwi6THgTGk7MPza+9iZmZtyXmCnYjzBM3MWs55gmZmZhW4CJqZWdNyETQzs6blImhmZk3LRdDMzJqWi6CZmTWtuV4EJa0g6VpJE/J39W6TtI6kHvk7dMW+gyQNzK8vK0QTjZL0QG4/SNLkQvsVFY65bo5EGiXpSUkX5fZtJd1a1vcySXvn1wtJOjvP9RlJN+X0h1Lf6XnMxyX9XdKiDbaXfo5t29+umZnVMleXTZMk4Abg8ojYN7f1BpYHXmxgiKNK0URlhkbEETX2OxcYEhE35WNu1OCUfw8sBqwTEdMlfQ+4XtIWkb5wOS0ieucxrwIOBc5qpN3MzDre3L4S3A74JCIuLDVExKiIGN7Ox10ReKlwzHoJEeSrt+8BR+a1RomIS4GPgK9W2GU4leOXqrWbmVkHm9tFcEPgkRrbexZvF5KuoIrOKGy/qtDer9D+vQrjDgHulnS7pCMlfb6wbZuyY5aCdNcCXoiId8vGGgn0KjZIWgDYlbL4pQrt3cpuh/ar8bswM7M2Nq+nSEwo3i6UNKhse6tuh0bEpZLuIGUA7g78UNImefPwiNitcMzLSi+BSmvMFdu75cIJ6YrvL3Xa694Ozan2AwBWW221Wl3NzKyF5nYRHAfsPTcOnNPjLwEuyR/A2bDOLs8Cq0taLCLeK7T3AW7Jr6sVtVY/+3OUkplZ+5nbt0PvBhaWdEipQdIXqqS7txlJu0haML9eAVgaeLnWPhHxPnA5cJak+fO+3wUWJZ2HmZl1MnO1COZPVO4J7Ji/djCOFGP0SoNDnFH2TG2hBvfbCXhc0mjgDtJt1dca2O844EPgaUnPAN8G9ozWR3GUPxM8tZXjmJlZKzhKqRNxlJKZWcs5SsnMzKwCF0EzM2taLoJmZta0XATNzKxpuQiamVnTchE0M7Om5SJoZmZNa46KoKTlJV0t6TlJj0gaIWnPsj7nSHpZ0nyFtmLm31OSjixsG5T7j8qZfddL2qCwfZik8YUvmF9X2O8DScsV+k6tMu/ukv5U+oK+pHslbZG3Vcz4y8cdWRijb27budB3amFuVyjlE75TNt4OZcd5XNItZYt4m5lZB2j12qE5C/BGUhbgfrltdWamLpAL356kbMAvA8MKQwyNiCMkLQ2Ml3RdRJQyBIdExOA8Rj9S4sNGETE5b98/Iip9a3wK8AvgmDrT/zMwEVg7Ij6TtCawft5Wa53P5STtGhG3lxoi4g7SqjNIGgYMLM1N0raULchdUMwYvBw4HDi5zrzNzKwNzcmV4FeBj8uyAJ+PiPMKfbYDHgf+CPSvNEhEvElanHrFKtuHAncC+zUwp0tIMUpLVesgqSewBXBCRHyWj/FcRPyzgfHPAE5ooF9LjQBWbodxzcyshjkpgr2AR+v06Q9cQ0qP3620aHWRpNWARYAxNcZ5FFiv8P6qwu3FMwrtU0mF8Kd15j2qFIxbQa2MvxHAR5K2qzF+uW3KxutZ3JgX494euLkFY5qZWRtosyglSRcAW5OuDr+QF7P+GimJ/T1JD5IWri5dcfXLxWRd4JCI+LDW8GXvq90OBTgXGCXpzFaeSr3Yo5NIV4P1brmWVLsdWsoY7EEKFv53pZ2dJ2hm1n7m5EpwHClLD4CIOJx0RbNsbtoFWAIYK2kSqUAWb4kOjYhewDbAmTnSqJpNgScbmVREvA1cDRxWY96bFD+o0xIRcTfpynXL1uxfUCq2qwMLkZ4JVjreRRHRNyL6LrvsspW6mJlZK81JEbwbWETSjwptixZe9wcOjogeEdEDWAPYSVKxDxExAriSKrcwJe1FuoK8pgVzOwv4IRWudCNiAjAS+G3+cA+S1pa0ewvGPxk4ugX9q4qId4CfAAMr3S42M7P20+oimDP09gC+ImmipIdIobPH5EK3MzNvfZZCae8DvlFhuNOA70laLL8/svQVCeAA4KuFT4bCrM8E/1NhblNIzyEXrjL9g4EVgGcljQUuZmaGYd2Mv4i4DZhc3l5F+TPBvSuM9xgwGti3wTHNzKwNOE+wE3GeoJlZy8l5gmZmZrNzETQzs6blImhmZk3LRdDMzJqWi6CZmTUtF0EzM2taLoJmZta0ahZBSUsXvuT9WiHnb5SkqJWHJ2m0pGvy689JelPSEmV9bpS0j2bNFyz9bCCph6TH652EpENyjt84SdWWSytlDg6s0F4rQ7Bvfv19SWMljcnnvHt5n/x+xpw1M0/wMaXcxMGFfhXPud65mplZ26m5gHaOOeoNqYAAUws5f1Or5eFJWp9UYL8s6XMR8b6kO0krzFye+yxBWk90P2Afcr5g8fiSetQ7AUkL5OOuBbxHWouzpWoumi1pFeB4oE9EvCOpOzPXSK1neETsJqkb8JikGyLi/rxttnM2M7OO01a3Q8vz8PYjrQd6JzNDdq9h1mXB9gT+FREftMHxFwCWjmRSG4xXbjlSgZ0KEBFTI2JiSwaIiGnAKJwbaGY2z5jjIlglD68fMJRU+ErJEf8CNlNKkodUEIuLYvcruzXYrcEpLEDKIrxRNcJ066iVIQhpXc/XgYmSLpVUaf3TmiQtCawN3FtornvOkgZIGilp5OTJjS5XamZmjZiTIljKw3sTWIqchyfpC8DkiHgeuAvoI2nJiPiYVCj3lrQM6TbrnYXxhkZE78LPtAbncQrpqvNM4BZJi+bnjGfU2a9oWtmxhxY35gDeXYC9gaeBIfn2MEClxVeLbdtIGgO8BtwaEa8VttU9Z0cpmZm1nzkpgtXy8PoD6yllCE4AFgf2yttKt0T3Bm6KiE/m4PglOwN3RcQVpOSIv+fx/9YGY8+Qb7U+FBGnkM6hdE5vAksWui4FTCm8Hx4RGwMbAT+S1Lst52VmZq03x7dDy/LwFga+DWxcyBHcnZm3RO8h3RI8nJblA9byGPDd/PosYDGgFymtvU1IWklSn0JTb+D5/HoYcICUsgmBA0nnOYuIeJp01dpoIr2ZmbWzNvlgTCEPbx/g5Yh4ubD5XmADSStGxGfAP4ClmfXZGMz+fGyr3L6upJcKP98u2+9nQG9J44CHgDuAh4EhVaZ7QnG83FYvQ3BBYHD+msMo0jPPUgjwRaQPzYyWNBroDgymsgtJn5hdo845m5lZB3CeYCfiPEEzs5aT8wTNzMxm5yJoZmZNy0XQzMyalougmZk1LRdBMzNrWi6CZmbWtJqmCEpaQdK1kiZIekLSbZLWkdRL0t2Snpb0jKRflb74Xil6SdKkvOxbMYLpcUl/l7Robl9F0k15vAmSzpG0UN62rVIM1TcKY94qaduO+l2YmVnSFEUwF7UbgGER0TMiNgB+CSxPWs/01IhYB9gE2AqomklYprTm6IbAx8Ch+VjXAzdGxNrAOqQv0J9c2O8lUjSTmZnNRU1RBIHtgE8i4sJSQ0SMIhWo+yPiztz2AXAEcGwrjjGclGn4VeDDiLg0jzkdOBL4fulKkbS6zjuSdmzd6ZiZWVtoliK4IZXXEp1tjdGImAB0l7R4o4PnYN9dgbFVxnwXeIFUJEtOAk5o9BhmZtb2mqUIViMqRyGR22ttg5lxUiNJRe4vNcacpT0ihgNI2qbmBJ0naGbWbpqlCI4DNqvSPst6cpLWBKZGxHvMHpMEKaXi7fy6mEP445yZWGnMxYFVSdFSRSdT59mg8wTNzNpPsxTBu4GFJR1Sasjhv88AW0vaIbd1A84FTs/d7gW+KWmxvP1bwOj8nK+au4BFJX037zM/KfD3svzMcYb8LHJJ0gdyzMysgzVFEYwUlbEnsGP+ysI4YBDwCinv8ARJ40nP9B4Gzs/7jcmv78u3PQ8FDm7wWN+W9Awpif5D0qdRKzkZWGVOzs/MzFrHUUqdiKOUzMxazlFKZmZmFbgImplZ03IRNDOzpuUiaGZmTctF0MzMmpaLoJmZNS0XQTMza1rzVBEsy+e7RdLnc3sPSdPyttJPaUWW70saK2lM3m93SRfkPk+U7bd33ucmSSPKjn1ZaXuhbWqF4z8h6QpJCxb6LSBpiqRTyvYfJmlk4X3f3LZzYU5TJY3Pr69o41+pmZnVsMDcnkCZaRHRG0DS5cDhzMzhm1DaViJpFdLam30i4h1J3YFlI+KmvL0HcGtxv1xY+wBTJa0RERMbnNuEiOidl0H7N7APcFXethMwHthH0i9j1hUIlpO0a0TcXmqIiDuAO/J8hgEDI8Lfgjcz62Dz1JVgmRHAynX6LAe8B0wFiIipDRS1vYBbgGuBfVs6qbxu6ENlc+sPnENKktiybJczcGSSmdk8aZ4sgvlqa3tS6ntJz7LboduQwmlfByZKulTSNxoYvj9wTf7p34q5LQJsAfwrv++W53prlTFHAB9J2q6lx8rjO0rJzKydzGtFsJTP9yawFOm2Y8mEQmxR74gYnq/KdgH2Ji1UPUTSoGqDS1qeFGx7X0Q8DXwqacO8udIiqsW2noW5vZAX1wbYDbgnJ0T8A9gzF/GiVgfoOkrJzKz9zGtFsPRMcHVgIdIzwZoieSgiTiHd3tyrRvd+pOiiiZImAT2YeUt0luxASUsBUwr7lp5JrgVsKembub0/sEMe7xFgaWCWq76IuBtYhNlvlZqZ2Vw0rxVBACLiHeAnwMDipzDLSVpJUp9CU2/g+RpD9wd2iYgeEdGDFLRbKoLDgH6SFsrvDwLuqTC3V4FjgeNyWO7WwGqFMQ+n8m3Wk4Gja8zNzMw62Lz26dAZIuIxSaNJRWo4M29HllwC3AQMlrQSKbNvMinzbzb5k6KrAf8tHGOipHclbRERt0raDHhE0nRSCnzFsYAbSXmEPwXujoiPCttuAk6XtHDZ+dwmyQ/1zMzmIc4T7EScJ2hm1nLOEzQzM6vARdDMzJqWi6CZmTUtF0EzM2taLoJmZta0XATNzKxpNVQECxFHpZ8ekraV9I6kxyQ9JWlw2T575Hijp3LU0R65ff6ysUblGKKhefuwQrTQKEnX5fZBkj6QtFzhGFOrzLe7pD9KmpDn94ikQ/K2erFIW0t6KM/7KUkDysb+rlJk07i8/8DcfpmkiYV5P5DbD5I0Obc9JelISYvk1xsVxj1a0oWN/H2YmVnbaPTL8jMijkryl8+HR8RueRHpxyTdEBH3S9oEGAzsmL+Qvgbwb0nP5TU3exfGWZGUyvC7wvD7V4kWmgL8Ajimznz/DDwHrB0Rn0laFvh+YXvFWCRJKwBXA3tExKOSlgHukPRyRPxT0q7Az4CdIuKVvJj2dwrjHhUR11WYz9CIOELS0qTIpevyOH+Q9GVgJeCHQMXvsZiZWftok9uhETENGMXMeKGBwO9LsUb5z1OAo4r7SRJwOXBGRDzewKEuIS1ttlS1DpJ6ApsDJ0TEZ/n4kyPitArzLo9FOhy4LCIezdunkJY6OzZvP46U/fdK3v5hRFzcwLxLx3sTeBZYMSL+BbwKfBcYAgyKiP81OpaZmc25Rotgt8JtvhvKN0paElgbuDc39SItJl00MrcXHQl8CpxX1n5V4XhnFNqnkgrhT2vMtRcwulQAa1FZLFID896wwvaiMwrzvqp8o6TVSAtplxIofkZaU3TZiLiy3nzNzKxttfp2aLaNpDHAusCpEfFabhezRxPN0pZvmf4M+EJZEjtUvx0KcC4wStKZjUxc0vHAt4HlImKl3Fxah3Rt4LpCLFKleVOlrZJqt0P7KeUJrgscEhEfAuRbqneTsgirzX8AMABgtdVWa3AaZmbWiDm9HTo8IjYGNgJ+JKl3bh/H7M+3+gBPwIwg2quAwyLi9ZYcMCLeJj23O6xKlyeATSTNl/ufnAv44oU+1WKRKs17s9K88/bNWjLfbGhE9AK2Ac7Mzx5LPss/FTlP0Mys/bTVM8GnSc/8Sh9YGUyKGuoBMz5E80vgzML2/4uIqldAdZxF+iDJbFeyEfEs6RbmSfmDL6XbnqrQd0YsUm66ADioVMzzB1lOA07P208hJUSskLcvLOknjU46IkYAV1L7dq6ZmXWQtvye4IXAlyWtERGjSAXxFklPAbcAR0fEKKXYo8OAr5Z9TaL4DK34TPA/5QfKH1i5AVi4fFt2MCnc9llJjwD/ofonSm8EFpW0TS6KBwAX53k/AFwSEbfk495GKpT/kTSO9HywWIjPKDunhZjdacD3JC1WZT5mZtZBHKXUiThKycys5eQoJTMzs9m5CJqZWdNyETQzs6blImhmZk3LRdDMzJqWi6CZmTUtF0EzM2ta7V4EJS0v6WpJz+VcvxGS9szbtpUUkn5Q6L9pbhtYYaxBedtahbYjc1vf/H6SUn5h6Qvr5+b2Yt7faEnbF8bYXNK9SjmGT0n6s6RFcxbg+WVzGFY6VoX2SjmI50r6VaHf8ZIuqDCnRyV9sfW/aTMza6lGF9BulRyVdCNweUTsl9tWB75Z6DYW6Af8Jb/fFxhdY9ixuc9J+f3ezFzbs2S7vKpMuaMi4rq8mPVFwNqSlgf+DuwbESPynPcCWrOiS6WFv08gLfh9FWkh7oOBTSvMaSfgT8DGrTiumZm1QntfCX4V+DgiZiSmR8TzEVGMTnoBWCRfMQrYBbi9xpg3ArsDSFoTeAeY3MJ5jWDWDMHL87qeRHJdSxf2riYi3gWOB84nLbn267wIeLl7SQt6m5lZB2nvItgLeLSBfteR4o62yv0/qtH3XeBFSRsC/YGhFfrcU7gteWSF7buQiinUzwjsV1wPlNrp7xVzECPiGmBJYPEauYHfIF3lzkLSAEkjJY2cPLmltd7MzGpp19uh5fKzsK1JV4dfKGz6G6mYrQdcQyqGtVxLuiW6M7A98L2y7dVuh54h6XRgOWDLBqc9NCKOKJzDsBp9K+YgSloFWAEISd0jYmrZnE4gXc3+oHzfiLiIdOuWvn37eqFXM7M21N5XguNIOYIARMThpKI1SzBeDuP9BNgRuKuBcW8BvgO8kG83Nuoo0i3HE4DLC3NsTUZgS5wDDCIV+9+UzykiekfEjhHxeDvPw8zMCtq7CN5Net73o0LbolX6/ho4JiKm1xs0IqaRopFObumEIuIzUlGaT9LOpGd1B0raotRH0gFlwbetJmlX0pXnFcDvgD0lbdAWY5uZ2Zxp19uhERGS9gCGSDqadMvvfSpk+0XEAy0c+9oam++RVCqmYyLiuxXmdRIp43B7SfsCgyUtR0p5vxe4viXzya6SNC2/ngLsBpwN7B0ps+r9/Hs4n/ShITMzm4ucJ9iJOE/QzKzlnCdoZmZWgYugmZk1LRdBMzNrWi6CZmbWtFwEzcysabkImplZ05rniqCkPXM00nr5/baSbi3rc5mkvSXdkNfpfFbSO4V1O7eStJCksyVNkPSMpJvy8mWlMULSlYX3C0iaXDyWpD0kjcnxSmPzdx6RdEE+zhOSphWOu3dhboMknVI2796SnsyvK0Y+mZlZx+nQtUMb1B+4j7Q26KBaHSNiRi4hMDAidittkzSYFIe0TkRMl/Q94HpJW5S+uA5sKKlbXoFmR+Dlwv6bAIOBHSNioqQ1gH9Lei4v/4akHsCtEdG7sF9pDteQ0jCOK0x5X+Dqwvtqa5yamVkHmKeuBCV1B75EWkh63zkYZ1HSotpHlpZhi4hLSekUxZVabge+nl/3JxWukoHA7yNiYt5/InAKaf3RuiJiPPB2cTk2YB/S4t9mZjYPmKeKILAH8K+IeBp4S1KfOv2rWYvKi2uPJMU7lVwL7CtpEVKY7YOFbb2YPWKpfP96riEXc0lbAm9GxDOF7fUin8zMrB3Na0WwPzOvlK7N76ut61ZrvTdV2T5Le0SMAXrk49zWwBjVxq3mWmBvSfORiuE1Zdu3ywkSvSNiSKUBnCdoZtZ+5plngpKWJt2q3FBSAPOTCs4VpEDaoqVIC1RX8yywuqTFIuK9QnsfUgxT0c2kZ3/bAksX2seRAnTHlO3/RCPnAxARL0qaBHwF2Av4YqP7FsZwnqCZWTuZl64E9wauiIjVI6JHRKwKTCQVvJUkrQ8gaXVgE2BUtYEi4n1SXuBZkubP+32XFON0d1n3S4ATI6I81X0wcFz+8EvpQzC/BM5s4XldAwwBJkTESy3c18zM2tE8cyVIuiV5alnbP0i3EQ8ALs3P7j4BDo6Id+qMdxypkD0t6TPgKWDPKIvNyIXpnPKdI2KUpGOAWyQtmI97dESMauF5/T2P/+MK22pGPpmZWftylFIn4iglM7OWc5SSmZlZBS6CZmbWtFwEzcysabkImplZ03IRNDOzpuUiaGZmTctF0MzMmlabF0FJ0/OC0I9L+ntOdEDS1LJ+B0k6v/B+QM7te0rSQ5K2zu2tzgwsO97Ckm7MGX6PSVqzSr/LJP2wrG0PSbcVz0NSj5wl+JikJ/OcDyw7v8mF+Y6StEHe1kvS3ZKezvP+lSS15vdtZmat1x5XgtPygtAbAh8Dh9bbIWfw/RDYOiLWy/tcLWmFiNgz5/UdDAwvLDj9APB7ZmYGrg3cSMoMrFRQ9gHeiYiNSGuUvlVlOjOSHwoqLX4NaSm0TSNi/dznyJxbWDK0MN/eEfGEpG6k9UpPjYh1SEvAbQUcVut3ZGZmba+9b4cOJ8Ua1XMMcFQpYDYiHiWt/Xl4tR1akBlY8jGwsiRFxP8i4u0qQ/8HWE/SioXj7EAqsFVFxHPAz4Gf1OoH7AfcHxF35v0+AI4Ajq2zn5mZtbF2K4KSFgB2BUoLU3cr3hoETix0b012X6OZgSXPAZuRgnGrygX1etKVI8A3gXvK0iiqeRRYr/C+X9nt0G5UONeImAB0l7R4+YCOUjIzaz/tUQS75SI3EngB+Etun1a8NQj8us449bL7GsoMBMjF5zJSAeot6We5/TZJlQpm8ZZotVuh1eZUVH47dFqNeVOpPSIuioi+EdF32WWXbXAaZmbWiPZIkZiWi1xLPEG6SivGHNXL7mtJZuBGwOSIeEXSXsB/cmbh56sc435gRUml53Xlzwir2RR4sk6fccCXiw35QzpTG7zaNDOzNjKvfEXidOC0HKyLpN7AQcAfqu3QwszAZ0jP+Xrl/X4AnAHcXB6tlMcO4G95/Nsi4sN6J5DzBgcD59XpehWwtaQd8n7dgHNJvwMzM+tA80SeYETcLGll4IF8hfYecEBEvFpn10YzA/+Xv75wZf7k6DvA/sApku7NnzQtdw1wFLU/sNJT0mPAInnO5+UP55T0K33VIzssIh6QtDtwnqQLgPmBK4HzMTOzDuU8wU7EeYJmZi3nPEEzM7MKXATNzKxpuQiamVnTchE0M7Om5SJoZmZNy0XQzMyaVkNFUNIKkq7NkUVP5OXG1slxQiHpx4W+5+cYoQvyeplP5Mih0vqZe+e4oon5/WhJ2xf2Hyap4kdZJT2S45O+nyORxihFNu1e53iSdEKOLXpa0j3F5dIkTSqM93+SVi9sm65Z1/+c7XuDkraU9GDe/qSkQbl9kKSBZX0nSVqmbOxZYqfMzKxj1P2yfP5y+Q3A5RGxb27rDSwPvAi8AfxU0p8i4uPSfhFxeO7bA7i1uJSaUnTSURFxnaTtgIuAtevMowfwMrAccDzQJyLekdQdWDYibqpxvCNIy59tEhEfSNoJuDmvIFNaDWa7iJgi6bfACcAhub2RZeAuB/aJiNF59Zp16/QvmTG2pKtIEVJnNbivmZnNoUauBLcDPomIC0sNETEqIobnt5OBu4ADK+3cgBHAyg302xX4F6kIvgdMzXOZGhET6+x7DPDjHFtEjjF6gLRqTGvnU7Qc8Goee3pE1FrztJpGY6fMzKyNNFIEN2T2mKNypwK/KK3h2UK7UCerr9DvX8Bo4HVgoqRLJX2j1k5K8USfy3FFRdUil8rnM0sElKR+FfYZAoyXdIOkH0papIHzKc6xPHbKzMw6QJusHRoREyU9RAqMbdQZkk4nXUVtWaujpIWAVXJwLZJ2Ab4AbA8MkbRZRAxq4bTLI43ukbQ86fbuCYX2urdDI+LEfDtzJ9LvoD+wLfUjk0qxU5CuBP9S3lHSAGAAwGqrrVZrGmZm1kKNXAmOI8Uc1fN70m3HRj9xehTp9t8JpGdqtWwD3Fd6E8lDEXEKKeZor2o75tDd95XiiorKo5q2A1Ynne+JtFBETIiIP5IK8yZKiRhvAkuWdV0MeDu/LmYs/rj4TLUwrvMEzczaSSMF625gYUmlD4og6QuSvlLsFBFPkYrKbo0ePCI+A84B5pO0c42uuwC352OvJKlPYVtv4Pk6hzoDOFcptgilGKOtgavL5jMN+BnwXUlLNXoekr6eP0AE6QM+00mF7l7gm5IWy/2+BYzO6fVmZjaX1b0dGhEhaU/g7Pz1gA+BSaRiUe5k4LGWTCCPfxJwNHBHbv6npE/y6xFAD2Ym0S8IDJa0Up7LZNKnKms5j3RFNlbSdOA1YPdc9Mrn86qka4DDgd8x6y1LgH9FRPnXJL5Dui37AfApsH8udGMknQ/cpxQR9QZwcJ25mplZB5nno5QkrQJcHBG7zu25zG2OUjIzaznViFKaJ0J1a4mIl0ifnDQzM2tTXjbNzMyalougmZk1LRdBMzNrWi6CZmbWtFwEzcysabkImplZ02qXIlgtJ0/SKpJuyrl+EySdk9cFRdKikq7KuX6PS7pP0uqFhatfk/Ry4f1CkpaV9ImkH5Ydf0ZmX405bi7pXknjJT0l6c95DgdJmly2aPYGapvsxFGSHsj7Fo/zlKQj2/5vwszMammvK8HSmpgbAh8Dh+Zlxa4HboyItYF1gO6kVWYAfgq8HhEb5f1+ALxWWlsTuBAYUlhr82Pg28B/SQtWNywvlP134JiIWBdYn5RQsVjuMrRwnN6FaKRSduJCxfEi4vA8x68BEwr7XZe7HFVo26qw69C835eA4yWt2pLzMDOzOdMRt0NLOXlfBT6MiEsh5e4BRwLfz1eKK5JCc8nbx0fER3XG7g/8AlhFUksyAA8nhQSPyMeKiLguIl6vs9+cZidWFBFvAs+SfgdmZtZB2rUIluXk9aIslzAnPLxAKpKXAMdIGiHpJEn1kuZXBVaIiIeAvwGVcv6qqZeR2K/sdmi3wrbWZCeeURjrqvKNklYDFgHGtGBMMzObQ+1VBEuLTo8kFbm/MHt+X4lIF2OjgDVJiQ9LAQ9LWr/GMfYlFT+Aa2nhLdE6ym+HzlhoO6fYtzQ7sXg7tJhm30/SOOA54JyI+LB8R0kDJI2UNHLy5MmtPR8zM6ugvZ8JFnPyxgGzLGCqlPq+KjABICKmRsT1EXEY8FfSM7Zq+gMHSZoE3EzK8Kt59VjQaEZiNS3NTqxmaET0IuUlnilphfIOzhM0M2s/HfkVibuARSV9FyDfTjwTuCwiPpD0JUlL5m0LARtQJSdQ0rrA5yJi5YjoERE9gFLAbiPOBw6UtEVhzAMqFaFKWpOdWGe8EcCVpA8HmZlZB+mwIhgps2lP4NuSngGeJuUB/jJ36Qn8n6SxpEzCkcA/qgzXH7ihrO0fzHpLdIykl/LPWWVzeZ1UMAfnr0g8Sboaezd3KX8mWPxEZ8nJwCr1zxyY9ZngqPJPl2anAd9TDuA1M7P2N8/nCdpMzhM0M2u5WnmCXjHGzMyalougmZk1LRdBMzNrWi6CZmbWtFwEzcysabkImplZ02pREawRkVRsv0XS5wv79JJ0t6SnlSKUfpUTJYpxQo/lbXdU+k6ekimFL9OvmGONti70mSxpaUmDJA0s239GtJKk4yWNkzQmz3kLykjaUtKDefuTkgaVbT9HKdZpvkLbQZLOrzDWJKV4qNJ3BM9t5BhmZtb+WnolOFtEUoX2t0gpDeSFp28GTo2IdYBNgK2AwwpjDo2ITXO80qnA9eVrhuYv2j8IfDE3bUX6Qv1W+TjrAlNyGkNVkr5IWuWlT0RsDOwAvFih6+XAgBxztCEz1yglF749835frnW8gu0Ky8j9pN4xzMysY8zJ7dBSRFK5EUAp1mg/4P6IuBMgIj4AjgCOrTRgRNwDXAQMqLD5fnLRy3+exaxF8YEG5rwiqVh+lI83JSJeqdBvOeDV3Gd6IU8QYDvgceCPzNmi3bWOYWZmHaBVRbAsIqnYPj+wPenqDyrHJ00AuufFsyt5FFivQvsDzCyCmwM3khbfJrffX+h7ZHGZMmCl3H4nsGq+NfsHSV+pMochwHhJN0j6oaRFCtv6A9eQlm3bTdKCVcYouqcwn1KCfK1jmJlZB2hpEawUkVRsf5MUg/Tv3F4tPoka7arS/hCwqaTPAQtGxFTgOUlrMfuVYDGBvjfwCqSUClJ6xABSQO5QSQfNNrGIE0mJF3eSrmb/BTMW9v4acGPOQnwQ2KnKfIuKt0OH1DrGbL8MRymZmbWb1j4TLEYkzWgHVgcWIj8TpHJ80prA1Ih4r8oxNgWelLRq4erp0Hwr9Vng+6SrRYD/korScsD4Rk4g33ocFhG/Id2a3atKvwkR8UfSle0mkpYGdgGWAMYqRThtzRzcEq1yjPI+jlIyM2snbfoViYh4B/gJMDDfJrwK2FrSDjDjgzLnAqdX2j/fnhwAXBwRLxYK7oW5y/3Az0jPHcl//hT4bzSwErikdTVr5mBvKsQ1Sfp66ROswNrAdOBtUsE7uBDftAawU+lTsi1R4xhmZtZBFmjrASPiMUmjgX0j4kpJuwPnSboAmJ+Um1f8KkG//FWHRYGJwF4R8WSV4e8nFb1SEXyUFGf05wan1z3P5fPAp6Qry0ofwvkOMETSB7nf/sDCwM7ADwvn+r6k+4Bv5KaDJO1RGGfL/Oc9kqbn12Mi4ruVjhER0zEzsw7jKKVOxFFKZmYtJ0cpmZmZzc5F0MzMmpaLoJmZNS0/E+xEJL1Hg18F6UKWAabM7UnMBc143j7n5tHR5716RFT8jlmbfzrU2tX4ag93uypJI5vtnKE5z9vn3DzmpfP27VAzM2taLoJmZta0XAQ7l4vm9gTmgmY8Z2jO8/Y5N4955rz9wRgzM2tavhI0M7Om5SLYSUjaRdJ4Sc9KqhhKPC+RdImkNyQ9XmhbStK/JT2T/1yysO24fG7jJe1caN9M0ti87dzSouOSFpY0NLc/KKlHYZ8D8zGekXRgB50yOfnkHklPShon6adNct6LSHpI0uh83r9thvPOx55f0mOSbs3vu/Q5S5qU5zpK0sgucc4R4Z95/Ie08PgEYE1SVNVoYIO5Pa86c/4y0Ad4vNB2OnBsfn0scFp+vUE+p4VJyRwTgPnztoeAL5JyJm8Hds3thwEX5tf7AkPz66WA5/KfS+bXS3bQOa8I9MmvFwOezufW1c9bQPf8ekFSzuaWXf288/F/DlwN3Nok/41PApYpa+vU59wh/6H4Z47/w/sicEfh/XHAcXN7Xg3MuwezFsHxwIr59Yqk7z3Odj7AHfmcVwSeKrT3B/5U7JNfL0D64q2KffK2PwH959L53wTs2EznTUqDeRTYoqufNynB5i7gq8wsgl39nCcxexHs1Ofs26Gdw8rAi4X3L+W2zmb5iHgVIP+5XG6vdn4r59fl7bPsExGfAu8AS9cYq0Pl2zibkq6Kuvx559uCo4A3gH9HRDOc99nA0cBnhbaufs4B3CnpEUmlGLpOfc5eMaZzUIW2rvSx3mrnV+u8W7NPh5DUHfgH8LOIeFeqNKXUtUJbpzzvSFmYvZWyOm+QtGGN7p3+vCXtBrwREY9I2raRXSq0dapzzr4UEa9IWg74t6SnavTtFOfsK8HO4SVg1cL7VYBX5tJc5sTrklYEyH++kdurnd9L+XV5+yz7SFoAWAJ4q8ZYHULSgqQCeFVEXJ+bu/x5l0TE28AwYBe69nl/CfimpEnAtcBXJf2Vrn3ORMQr+c83gBuAzens59wR95H9M8f34RcgPQheg5kfjOk1t+fVwLx7MOszwTOY9QH66fl1L2Z9gP4cMx+gP0z6kEXpAfrXcvvhzPoA/W/59VLARNLD8yXz66U66HwFXAGcXdbe1c97WeDz+XU3YDiwW1c/78L5b8vMZ4Jd9pyBzwGLFV4/QPo/O536nDvsPxT/zPF/gF8jfdpwAnD83J5PA/O9BngV+IT0/+J+QLq3fxfwTP5zqUL/4/O5jSd/Uiy39wUez9vOZ+YCD4sAfweeJX3SbM3CPt/P7c8C3+vAc96adItmDDAq/3ytCc57Y+CxfN6PA7/O7V36vAvH35aZRbDLnjPp0+mj88848r9Dnf2cvWKMmZk1LT8TNDOzpuUiaGZmTctF0MzMmpaLoJmZNS0XQTMza1ougmZm1rRcBM3MrGm5CJqZWdP6fyE8a1nBJ6O7AAAAAElFTkSuQmCC",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(\n",
" df['department']\n",
" .value_counts(ascending=True)\n",
" .plot.barh(title='Total transactions by department', figsize=(6,8))\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately, we see a lot of very small values that overcrowds the plot. We can make some small adjustments to our code to leave all department values for those departments in the top 10 as is but for all departments not in the top 10 we can condense them down to an 'Other' category.\n",
"\n",
"```{note}\n",
"We will discuss the [`.where()` method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.where.html) in module 6. For now just realize its a way to apply an if-else condition to a Series.\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"top10 = df['department'].value_counts().index[:10]\n",
"isin_top10 = df['department'].isin(top10)\n",
"\n",
"(\n",
" df['department']\n",
" .where(isin_top10, 'Other')\n",
" .value_counts(ascending=True)\n",
" .plot.barh(title='Total transaction sales by department', figsize=(6,6))\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{admonition} Knowledge check\n",
":class: attention\n",
"1. Create a histogram for the `quantity` column. Remove any zero quantities and/or adjust the axis to make the plot more informative.\n",
"2. Compute the sum of `quantity` for each `store_id`. Now create density plot and box plot. Compare these plots to the summary statistics provided by `.describe()`.\n",
"3. Use `.resample()` to compute the sum of `quantity` for each day. Plot the results to assess if there is similar pattern as we saw with `sales_value`.\n",
"4. Use a bar plot to plot the total transaction quantities by `department`. Are the results similar to what we saw with total `sales_value` by department?\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting a DataFrame\n",
"\n",
"Once we understand the basics of `.plot` with a Series we can extend this to a DataFrame easily. The primary difference is that the `.plot` sub-methods for a DataFrame allow us to plot multiple variables at once (i.e. `x` and `y`) and there are a few plotting methods that work for DataFrames that are not available for Series (i.e. `.scatterplot`, `.hexbin`)\n",
"\n",
"For example, say we want to plot `sales_value` vs `quantity` across all transactions. The main difference we see below is that we specify which variables in our DataFrame to plot with `x` and `y`."
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x='quantity', y='sales_value', title='Sales versus quantity', figsize=(8,4));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's important to point out that although we used a few plots that are usually considered univariate in the Series section (i.e. `.plot.hist`, `.plot.bar`), we can still use these same methods with DataFrames. For example, say we wanted to compute the sum of `sales_value` at the department level but we want the output to be a DataFrame (which is very common)."
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
department
\n",
"
sales_value
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
GROCERY
\n",
"
1310385.28
\n",
"
\n",
"
\n",
"
1
\n",
"
DRUG GM
\n",
"
337458.00
\n",
"
\n",
"
\n",
"
2
\n",
"
FUEL
\n",
"
210337.18
\n",
"
\n",
"
\n",
"
3
\n",
"
PRODUCE
\n",
"
193993.90
\n",
"
\n",
"
\n",
"
4
\n",
"
MEAT
\n",
"
169014.96
\n",
"
\n",
"
\n",
"
5
\n",
"
MEAT-PCKGD
\n",
"
120369.80
\n",
"
\n",
"
\n",
"
6
\n",
"
DELI
\n",
"
86380.11
\n",
"
\n",
"
\n",
"
7
\n",
"
MISCELLANEOUS
\n",
"
46790.04
\n",
"
\n",
"
\n",
"
8
\n",
"
PASTRY
\n",
"
38328.18
\n",
"
\n",
"
\n",
"
9
\n",
"
NUTRITION
\n",
"
35779.51
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" department sales_value\n",
"0 GROCERY 1310385.28\n",
"1 DRUG GM 337458.00\n",
"2 FUEL 210337.18\n",
"3 PRODUCE 193993.90\n",
"4 MEAT 169014.96\n",
"5 MEAT-PCKGD 120369.80\n",
"6 DELI 86380.11\n",
"7 MISCELLANEOUS 46790.04\n",
"8 PASTRY 38328.18\n",
"9 NUTRITION 35779.51"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dept_sales = (\n",
" df\n",
" .groupby('department', as_index=False)\n",
" .agg({'sales_value': 'sum'})\n",
" .nlargest(10, 'sales_value')\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"dept_sales"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can still plot the results by assigning the columns of interest to the `x` and `y` parameters in `.plot.barh`.\n",
"\n",
"```{note}\n",
"It's common to use `.sort_values` just before bar plots to adjust the order of the bars.\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(\n",
" dept_sales\n",
" .sort_values('sales_value')\n",
" .plot.barh(x='department', y='sales_value', color='red')\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A great advantage of the `.plot` methods is that they allow us to easily plot more than one column of interest. For example, say we compute the total \n",
" for the given departments."
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
department
\n",
"
sales_value
\n",
"
quantity
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
GROCERY
\n",
"
1310385.28
\n",
"
693486
\n",
"
\n",
"
\n",
"
1
\n",
"
DRUG GM
\n",
"
337458.00
\n",
"
111003
\n",
"
\n",
"
\n",
"
2
\n",
"
PRODUCE
\n",
"
193993.90
\n",
"
111279
\n",
"
\n",
"
\n",
"
3
\n",
"
MEAT
\n",
"
169014.96
\n",
"
36421
\n",
"
\n",
"
\n",
"
4
\n",
"
MEAT-PCKGD
\n",
"
120369.80
\n",
"
43233
\n",
"
\n",
"
\n",
"
5
\n",
"
DELI
\n",
"
86380.11
\n",
"
22048
\n",
"
\n",
"
\n",
"
6
\n",
"
PASTRY
\n",
"
38328.18
\n",
"
16601
\n",
"
\n",
"
\n",
"
7
\n",
"
NUTRITION
\n",
"
35779.51
\n",
"
15188
\n",
"
\n",
"
\n",
"
8
\n",
"
SEAFOOD-PCKGD
\n",
"
18996.37
\n",
"
4207
\n",
"
\n",
"
\n",
"
9
\n",
"
FLORAL
\n",
"
12494.58
\n",
"
1798
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" department sales_value quantity\n",
"0 GROCERY 1310385.28 693486\n",
"1 DRUG GM 337458.00 111003\n",
"2 PRODUCE 193993.90 111279\n",
"3 MEAT 169014.96 36421\n",
"4 MEAT-PCKGD 120369.80 43233\n",
"5 DELI 86380.11 22048\n",
"6 PASTRY 38328.18 16601\n",
"7 NUTRITION 35779.51 15188\n",
"8 SEAFOOD-PCKGD 18996.37 4207\n",
"9 FLORAL 12494.58 1798"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dept_totals = (\n",
" df\n",
" .query(\"department != 'FUEL' & department != 'MISCELLANEOUS'\")\n",
" .groupby('department', as_index=False)\n",
" .agg({'sales_value': 'sum', 'quantity': 'sum'})\n",
" .nlargest(10, 'sales_value')\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"dept_totals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can actually plot both the `sales_value` and `quantity` columns in the same bar plot by passing a list to `y`:"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(\n",
" dept_totals\n",
" .sort_values('sales_value')\n",
" .plot.barh(x='department', y=['sales_value', 'quantity'])\n",
" .legend(loc='lower right')\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This feature is especially handy when plotting multiple line charts. For example, say we have daily totals computed for each of the discount variables:"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
retail_disc
\n",
"
coupon_disc
\n",
"
coupon_match_disc
\n",
"
\n",
"
\n",
"
transaction_timestamp
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
2017-01-01
\n",
"
495.69
\n",
"
4.45
\n",
"
1.45
\n",
"
\n",
"
\n",
"
2017-01-02
\n",
"
613.58
\n",
"
4.15
\n",
"
2.15
\n",
"
\n",
"
\n",
"
2017-01-03
\n",
"
601.23
\n",
"
8.95
\n",
"
3.75
\n",
"
\n",
"
\n",
"
2017-01-04
\n",
"
712.03
\n",
"
3.85
\n",
"
1.85
\n",
"
\n",
"
\n",
"
2017-01-05
\n",
"
724.14
\n",
"
14.60
\n",
"
3.09
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" retail_disc coupon_disc coupon_match_disc\n",
"transaction_timestamp \n",
"2017-01-01 495.69 4.45 1.45\n",
"2017-01-02 613.58 4.15 2.15\n",
"2017-01-03 601.23 8.95 3.75\n",
"2017-01-04 712.03 3.85 1.85\n",
"2017-01-05 724.14 14.60 3.09"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_daily_discounts = (\n",
" df\n",
" .query(\"department == 'GROCERY'\")\n",
" .set_index('transaction_timestamp')\n",
" .loc[:, ['retail_disc', 'coupon_disc', 'coupon_match_disc']]\n",
" .resample('D')\n",
" .sum()\n",
")\n",
"\n",
"total_daily_discounts.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we apply `.plot.line()` to this DataFrame then by default it will plot each of the numeric variables on the same plot."
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"total_daily_discounts.plot.line(logy=True, figsize=(10, 4));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In fact, this same logic applies to many of the univariate plots such as histograms, denisty plots, box plots, etc."
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"total_daily_discounts.plot.kde(logx=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{admonition} Knowledge check\n",
":class: attention\n",
"1. Compute the average `sales_value` and `quantity` by `household_id`. Create a density plot that plots both these columns.\n",
"2. Use a bar plot to assess whether married versus unmarried customers produce more transactions. Now do the same to assess which age groups produce the most transactions.\n",
"3. Use `.resample()` to compute the sum of `quantity` and `sales_value` for each month. Plot the results to assess which months have the largest `quantity` and `sales_value`s.\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Under the hood - Matplotlib\n",
"\n",
"Underneath the hood Pandas is using [Matplotlib](https://matplotlib.org/) to create the plots. Matplotlib is the most tried-and-true, mature plotting library in Python; however, its a bit more difficult to digest Matplotlib which is why I first introduce plotting with Pandas.\n",
"\n",
"In the next lesson we will dig into Matplotlib because, with it being the most popular plotting library in the Python ecosystem, it is important for you to have a baseline understanding of its capabilities. But one thing I want to point out here is, since Pandas builds plots based on Matplotlib, we can actually use Matplotlib in conjunction with Pandas to advance our plots.\n",
"\n",
"For example, Matplotlib provides many [style options](https://www.dunderdata.com/blog/view-all-available-matplotlib-styles) that can be used to beautify our plots. If you are familiar with [fivethirtyeight.com](https://fivethirtyeight.com/) you'll know that most of their visualizations have a consistent theme. We can use Matplotlib to change the style of our plots to look like fivethirtyeight plots."
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.style.use('fivethirtyeight')\n",
"\n",
"median_sales_by_weekday.plot.bar(title='Median total sales by weekday', figsize=(8,4));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We may also want to refine our tick marks so that they are formatted in the units of interest. For example, below we use Matplotlib's ticker module to format our y-axis to be in dollar and comma formatted units:"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.ticker as mtick\n",
"\n",
"tick_format = mtick.StrMethodFormatter('${x:,.0f}')\n",
"\n",
"(\n",
" median_sales_by_weekday\n",
" .plot.bar(title='Median total sales by weekday', xlabel='', figsize=(8,4))\n",
" .yaxis.set_major_formatter(tick_format)\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll explore more Matplotlib capabilities in the next lesson but for now, happy Pandas plotting!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"```{admonition} Questions:\n",
":class: attention\n",
"1. Identify all different products that contain “pizza” in their `product_type` description.\n",
"2. Use a bar plot to assess whether married versus unmarried customers purchase more pizza products.\n",
"3. Create a scatter plot to assess the `quantity` versus `sales_value` of pizza products sold.\n",
"4. Use `.resample` to compute the total `quantity` of pizza `product_type`s for each day of the year. Plot the results to assess if there is a pattern.\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing environment"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The watermark extension is already loaded. To reload it, use:\n",
" %reload_ext watermark\n",
"Python implementation: CPython\n",
"Python version : 3.9.12\n",
"IPython version : 8.2.0\n",
"\n",
"jupyterlab : 3.3.2\n",
"pandas : 1.4.2\n",
"completejourney_py: 0.0.3\n",
"matplotlib : 3.5.1\n",
"\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -v -p jupyterlab,pandas,completejourney_py,matplotlib"
]
}
],
"metadata": {
"interpreter": {
"hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}