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Add exercises on pandas operations
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‎notebooks/02.1 - Exercises on pandas operations.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercises on pandas operations\n",
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"\n",
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"\n",
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"This notebooks contains some exercises to practice the concepts learned in the notebook [02 - pandas operations](02%20-%20pandas%20operations.ipynb) as well as [01 - pandas basics](01%20-%20pandas%20basics.ipynb).\n",
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"\n",
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"#### Instructions & suggestions\n",
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"\n",
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"- Run the first cell below these instructions to load the data\n",
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"- For more complex questions, you can iteratively build your solution starting from the simpler pieces \n",
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"- There are enough questions to go overtime, feel free to take some of them home"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"clients_file = '../data/fake_shop/fake_clients.csv'\n",
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"transactions_file = '../data/fake_shop/fake_transactions.csv'\n",
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"\n",
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"clients = pd.read_csv(clients_file, parse_dates=['date_of_birth'])\n",
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"transactions = pd.read_csv(transactions_file, parse_dates=['date'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### List the number of customers per city"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Which product brings in the highest revenue?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### List the revenue (sum of the total) per day"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### List the number of transactions per day"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plot the number of clients per city (in alphabetical order)\n",
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"\n",
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"Plot with `kind='bar'` to product a bar chart."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### List the revenue (sum of the total) per city"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### List the number of transactions per client\n",
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"\n",
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"Show the client name and the number."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plot the revenue (sum of the total) per city\n",
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"\n",
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"- Plot using `kind='bar'` to product a bar chart.\n",
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"- Add the title \"Revenue per city\" to the plot\n",
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"- Add the label \"Revenue\" to the Y-axis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Which product has sold the highest number of units?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plot the total revenue per product\n",
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"\n",
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"Plot using `kind='bar'` to product a bar chart."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### For each city, list the revenue (sum of total) per product "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### For each different day, what's the average transaction total?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### For each different day, show the minimum and maximum revenue on a single transaction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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