03-pandas.ipynb

{
 "cells": [
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/\n",
    "\n",
    "Pandas data-manipulation capabilities are built on top of NumPy, utilizing its fast array processing, and its graphing capabilities are built on top of Matplotlib.\n",
    "\n",
    "* \"pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.\"\n",
    "\n",
    "* It may be one of the most widely used tools for data munging\n",
    "  * present data in nice formats\n",
    "  * multiple convenient methods for filtering data\n",
    "  * work with a variety of data formats (CSV, Excel, …)\n",
    "  * convenient functions for quickly plotting data\n",
    "\n",
    "* Name comes from panel data, also play on python data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = pd.Series(['And Now for Something Completely Different',\n",
    "          'Monty Python and the Holy Grail',\n",
    "          'Monty Python\\'s Life of Brian',\n",
    "          'Monty Python Live at the Hollywood Bowl',\n",
    "          'Monty Python\\'s The Meaning of Life',\n",
    "          'Monty Python Live (Mostly)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = [1971,1975,1979,1982,1983,2014]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "production_budget = ['100000', '400000', '4000000', None, '9000000', None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# taken from https://www.the-numbers.com but make no claims to verifying the numbers\n",
    "box_office = [np.nan, 5028948, 20515486, 327958, 14929552, 2215461]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Year': year,\n",
    "                   'Titles': titles,\n",
    "                   'Budget': production_budget,\n",
    "                   'Gross': box_office})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will come back to nulls and Dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(axis=0, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by='Gross')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by='Gross',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting\n",
    "loc, iloc, at, iat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Titles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[1,['Titles','Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[0:2,['Titles','Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[1,'Titles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.at[1,'Titles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[1,'Titles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[0:2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'] > 1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something's not right..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'] > 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'] = df['Budget'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'] > 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['Budget'] > 1000000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['Budget'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Budget'].isna()].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# might want to do this\n",
    "# df[df['Budget'].isna()] = 0\n",
    "# but no!\n",
    "# that will set entire rows to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['Budget'].isna()] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['Budget'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'].fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'].fillna(value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gross']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gross'].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preview of aggregate calculations\n",
    "df['Gross'].fillna(value=df['Gross'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gross'] = df['Gross'].fillna(value=df['Gross'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.to_datetime(df['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year'] = pd.to_datetime(df['Year'],format='%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Gross']/df['Budget'] > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Profit Factor'] = df['Gross']/df['Budget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='Year',y='Gross')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x='Year',y='Gross',kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating values and aggregating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Budget'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby('Budget')['Gross'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4group = pd.DataFrame({'folks':['a','b','c','d','e','f'],\n",
    "                         'python skills':[1,2,1,3,3,1],\n",
    "                         'monty python knowledge':[3,1,1,2,2,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4group.groupby('python skills').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4group.groupby('python skills')['monty python knowledge'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final fun - estimating $\\pi$ (again)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fraction of sample points that make it into the circle is:\n",
    "\n",
    "$$\\frac{N_{inside}}{N_{total}} = \\frac{\\pi r^2}{4 r^2}$$\n",
    "\n",
    "so we can use our sample to calculate $\\pi$ via:\n",
    "\n",
    "$$\\pi = 4 \\frac{N_{inside}}{N_{total}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.uniform(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_sample = pd.DataFrame(columns=['x','y','in_circle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.uniform(0,1,1000)\n",
    "y = np.random.uniform(0,1,1000)\n",
    "in_circle = (((x-0.5)**2 + (y-0.5)**2) < 0.5**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_sample['x'] = x\n",
    "pi_sample['y'] = y\n",
    "pi_sample['in_circle'] = in_circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_sample[pi_sample['in_circle'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_sample.groupby('in_circle').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pi_sample.groupby('in_circle')['x'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d[True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_sample.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pi_sample.groupby('in_circle')['x'].count()[True] / pi_sample.count()['in_circle'] * 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pi_estimate(nums = 1000):\n",
    "    df = pd.DataFrame(columns=['x','y','in_circle'])\n",
    "    df['x'] = np.random.uniform(0,1,nums)\n",
    "    df['y'] = np.random.uniform(0,1,nums)\n",
    "    df['in_circle'] = (((df['x']-0.5)**2 + (df['y']-0.5)**2) < 0.5**2)\n",
    "    circle_count = df.groupby('in_circle')['x'].count()[True]\n",
    "    total_count = nums\n",
    "    estimated_pi = 4 * circle_count / total_count\n",
    "    print('pi = '+str(estimated_pi))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testpi = pi_estimate(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testpi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/\n",
    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html"
   ]
  }
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