{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Version 1.0.3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas basics " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hi! In this programming assignment you need to refresh your `pandas` knowledge. You will need to do several [`groupby`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html)s and [`join`]()`s to solve the task. " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "\n", "from grader import Grader" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DATA_FOLDER = '../readonly/final_project_data/'\n", "\n", "transactions = pd.read_csv(os.path.join(DATA_FOLDER, 'sales_train.csv.gz'))\n", "items = pd.read_csv(os.path.join(DATA_FOLDER, 'items.csv'))\n", "item_categories = pd.read_csv(os.path.join(DATA_FOLDER, 'item_categories.csv'))\n", "shops = pd.read_csv(os.path.join(DATA_FOLDER, 'shops.csv'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset we are going to use is taken from the competition, that serves as the final project for this course. You can find complete data description at the [competition web page](https://www.kaggle.com/c/competitive-data-science-final-project/data). To join the competition use [this link](https://www.kaggle.com/t/1ea93815dca248e99221df42ebde3540)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will create a grader instace below and use it to collect your answers. When function `submit_tag` is called, grader will store your answer *locally*. The answers will *not* be submited to the platform immediately so you can call `submit_tag` function as many times as you need. \n", "\n", "When you are ready to push your answers to the platform you should fill your credentials and run `submit` function in the last paragraph of the assignment." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grader = Grader()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Task" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with a simple task. \n", "\n", "
    \n", "
  1. Print the shape of the loaded dataframes and use [`df.head`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html) function to print several rows. Examine the features you are given.
    2. \n", "
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datedate_block_numshop_iditem_iditem_priceitem_cnt_day
002.01.201305922154999.001.0
103.01.20130252552899.001.0
205.01.20130252552899.00-1.0
306.01.201302525541709.051.0
415.01.201302525551099.001.0
510.01.20130252564349.001.0
602.01.20130252565549.001.0
704.01.20130252572239.001.0
811.01.20130252572299.001.0
903.01.20130252573299.003.0
1003.01.20130252574399.002.0
1105.01.20130252574399.001.0
1207.01.20130252574399.001.0
1308.01.20130252574399.002.0
1410.01.20130252574399.001.0
\n", "
" ], "text/plain": [ " date date_block_num shop_id item_id item_price item_cnt_day\n", "0 02.01.2013 0 59 22154 999.00 1.0\n", "1 03.01.2013 0 25 2552 899.00 1.0\n", "2 05.01.2013 0 25 2552 899.00 -1.0\n", "3 06.01.2013 0 25 2554 1709.05 1.0\n", "4 15.01.2013 0 25 2555 1099.00 1.0\n", "5 10.01.2013 0 25 2564 349.00 1.0\n", "6 02.01.2013 0 25 2565 549.00 1.0\n", "7 04.01.2013 0 25 2572 239.00 1.0\n", "8 11.01.2013 0 25 2572 299.00 1.0\n", "9 03.01.2013 0 25 2573 299.00 3.0\n", "10 03.01.2013 0 25 2574 399.00 2.0\n", "11 05.01.2013 0 25 2574 399.00 1.0\n", "12 07.01.2013 0 25 2574 399.00 1.0\n", "13 08.01.2013 0 25 2574 399.00 2.0\n", "14 10.01.2013 0 25 2574 399.00 1.0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transactions.head(15)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use your `pandas` skills to get answers for the following questions. \n", "The first question is:\n", "\n", "1. ** What was the maximum total revenue among all the shops in September, 2014?** \n", "\n", "\n", "* Hereinafter *revenue* refers to total sales minus value of goods returned.\n", "\n", "*Hints:*\n", "\n", "* Sometimes items are returned, find such examples in the dataset. \n", "* It is handy to split `date` field into [`day`, `month`, `year`] components and use `df.year == 14` and `df.month == 9` in order to select target subset of dates.\n", "* You may work with `date` feature as with strings, or you may first convert it to `pd.datetime` type with `pd.to_datetime` function, but do not forget to set correct `format` argument." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7982852.2\n", "Current answer for task max_revenue is: 7982852.2\n" ] } ], "source": [ "#transactions['date'] = pd.to_datetime(transactions['date'])\n", "#transactions = transactions.set_index(transactions['date'])\n", "#transactions = transactions.sort_index()\n", "#transactions.head()\n", "\n", "#sep_transacts = transactions['2014-09-01':'2014-09-30']\n", "#items_sold = sum(sep_transacts.item_price)\n", "#returned = []\n", "#for n in sep_transacts.item_cnt_day: \n", "# if n <0:\n", "# returned = returned.append(sep_transacts.item_price)\n", "#returned = sum(returned)\n", " \n", "#print(\"sold \" + items_sold)\n", "#print(\"returned \" + returned)\n", "\n", "trans = transactions[transactions['date'].str.endswith('09.2014')]\n", "max_revenue = (trans['item_price'] * trans['item_cnt_day']).groupby(trans['shop_id']).sum().max()\n", "print(max_revenue)\n", "#max_revenue = items_sold - returned\n", "grader.submit_tag('max_revenue', max_revenue)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Let's move on and answer another question:\n", "\n", "
    \n", "
  1. What item category generated the highest revenue in summer 2014?
    2. \n", "
\n", "\n", "* Submit `id` of the category found.\n", " \n", "* Here we call \"summer\" the period from June to August.\n", "\n", "*Hints:*\n", "\n", "* Note, that for an object `x` of type `pd.Series`: `x.argmax()` returns **index** of the maximum element. `pd.Series` can have non-trivial index (not `[1, 2, 3, ... ]`)." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n", "Current answer for task category_id_with_max_revenue is: 20\n" ] } ], "source": [ "# YOUR CODE GOES HERE\n", "trans = transactions[transactions['date'].str.match('.*0[6-8].2014$')]\n", "trans = trans[trans['item_cnt_day'] > 0]\n", "trans = pd.merge(trans, items, how='left', on='item_id')\n", "category_id_with_max_revenue = (trans['item_price'] * trans['item_cnt_day']).groupby(trans['item_category_id']).sum().argmax()\n", "\n", "print(category_id_with_max_revenue)\n", "grader.submit_tag('category_id_with_max_revenue', category_id_with_max_revenue)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
    \n", "
  1. How many items are there, such that their price stays constant (to the best of our knowledge) during the whole period of time?
    2. \n", "
\n", "\n", "* Let's assume, that the items are returned for the same price as they had been sold." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5926\n", "Current answer for task num_items_constant_price is: 5926\n" ] } ], "source": [ "# YOUR CODE GOES HERE\n", "num_items_constant_price = (transactions['item_price'].groupby(transactions['item_id']).nunique() == 1).sum()\n", "print(num_items_constant_price)\n", "grader.submit_tag('num_items_constant_price', num_items_constant_price)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember, the data can sometimes be noisy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
    \n", "
  1. What was the variance of the number of sold items per day sequence for the shop with `shop_id = 25` in December, 2014? Do not count the items, that were sold but returned back later.
    2. \n", "
\n", "\n", "* Fill `total_num_items_sold` and `days` arrays, and plot the sequence with the code below.\n", "* Then compute variance. Remember, there can be differences in how you normalize variance (biased or unbiased estimate, see [link](https://math.stackexchange.com/questions/496627/the-difference-between-unbiased-biased-estimator-variance)). Compute ***unbiased*** estimate (use the right value for `ddof` argument in `pd.var` or `np.var`). \n", "* If there were no sales at a given day, ***do not*** impute missing value with zero, just ignore that day" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: to_datetime is deprecated. Use pd.to_datetime(...)\n", " if __name__ == '__main__':\n" ] }, { "data": { "image/png": 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apDrgCuAUSUeRVD29CHwEICKelnQrSUN4J/DRiMikx/kY8FugErguIp4eqjybmQ1H2Wze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\nl5LHxm1JyeO18yY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "117167.702299\n", "Current answer for task total_num_items_sold_var is: 117167.702299\n" ] } ], "source": [ "shop_id = 25\n", "\n", "shop_id = 25\n", "\n", "trans = transactions[transactions['shop_id'] == shop_id]\n", "trans = trans[trans['date'].str.endswith('12.2014')]\n", "trans = trans['item_cnt_day'].groupby(trans['date']).sum()\n", "total_num_items_sold = trans.values\n", "days = trans.index.to_datetime()\n", "\n", "total_num_items_sold_var = trans.var()\n", "\n", "# Plot it\n", "plt.plot(days, total_num_items_sold)\n", "plt.ylabel('Num items')\n", "plt.xlabel('Day')\n", "plt.title(\"Daily revenue for shop_id = 25\")\n", "plt.show()\n", "\n", "print(total_num_items_sold_var)\n", "grader.submit_tag('total_num_items_sold_var', total_num_items_sold_var)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Authorization & Submission\n", "To submit assignment to Cousera platform, please, enter your e-mail and token into the variables below. You can generate token on the programming assignment page. *Note:* Token expires 30 minutes after generation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "STUDENT_EMAIL = carlosesva@outlook.com\n", "STUDENT_TOKEN = YTDNjxpYo94Psil5\n", "grader.status()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grader.submit(STUDENT_EMAIL, STUDENT_TOKEN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well done! :)" ] } ], "metadata": { "hw_version": "1.0.0", "kernelspec": { "display_name": "Python 3", "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }