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joaovicente/pandas-101.ipynb

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  1. joaovicente revised this gist Dec 9, 2020. 1 changed file with 1 addition and 0 deletions.
    1 change: 1 addition & 0 deletions pandas-101.ipynb
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    "\n",
    "* [pandas.pydata.org](https://pandas.pydata.org/)\n",
    "* [matplotlib.org](https://matplotlib.org/)\n",
    "* [15 ways to create a Pandas DataFrame](https://towardsdatascience.com/15-ways-to-create-a-pandas-dataframe-754ecc082c17)\n",
    "* [Using Pandas DataFrame guide by Shane Lynn](https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/)\n",
    "* [Don't compare averages by Martin Fowler](https://martinfowler.com/articles/dont-compare-averages.html)\n",
    "* [Choosing a good chart by Extreme Presentation](https://extremepresentation.com/wp-content/uploads/choosing-a-good-chart-09-1.pdf)"
  2. joaovicente revised this gist Dec 9, 2020. 1 changed file with 0 additions and 1 deletion.
    1 change: 0 additions & 1 deletion pandas-101.ipynb
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    "\n",
    "* [pandas.pydata.org](https://pandas.pydata.org/)\n",
    "* [matplotlib.org](https://matplotlib.org/)\n",
    "* [15 ways to create a Pandas DataFrame](https://towardsdatascience.com/15-ways-to-create-a-pandas-dataframe-754ecc082c17)\n",
    "* [Using Pandas DataFrame guide by Shane Lynn](https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/)\n",
    "* [Don't compare averages by Martin Fowler](https://martinfowler.com/articles/dont-compare-averages.html)\n",
    "* [Choosing a good chart by Extreme Presentation](https://extremepresentation.com/wp-content/uploads/choosing-a-good-chart-09-1.pdf)"
  3. joaovicente revised this gist Dec 9, 2020. 1 changed file with 1 addition and 0 deletions.
    1 change: 1 addition & 0 deletions pandas-101.ipynb
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    Original file line number Diff line number Diff line change
    @@ -855,6 +855,7 @@
    "\n",
    "* [pandas.pydata.org](https://pandas.pydata.org/)\n",
    "* [matplotlib.org](https://matplotlib.org/)\n",
    "* [15 ways to create a Pandas DataFrame](https://towardsdatascience.com/15-ways-to-create-a-pandas-dataframe-754ecc082c17)\n",
    "* [Using Pandas DataFrame guide by Shane Lynn](https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/)\n",
    "* [Don't compare averages by Martin Fowler](https://martinfowler.com/articles/dont-compare-averages.html)\n",
    "* [Choosing a good chart by Extreme Presentation](https://extremepresentation.com/wp-content/uploads/choosing-a-good-chart-09-1.pdf)"
  4. joaovicente revised this gist Oct 7, 2020. 1 changed file with 113 additions and 113 deletions.
    226 changes: 113 additions & 113 deletions pandas-101.ipynb
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    113 additions, 113 deletions not shown because the diff is too large. Please use a local Git client to view these changes.
  5. joaovicente revised this gist Oct 6, 2020. 1 changed file with 27 additions and 42 deletions.
    69 changes: 27 additions & 42 deletions pandas-101.ipynb
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    @@ -388,7 +388,7 @@
    },
    {
    "cell_type": "code",
    "execution_count": 5,
    "execution_count": 16,
    "metadata": {},
    "outputs": [
    {
    @@ -414,99 +414,87 @@
    " <th></th>\n",
    " <th>name</th>\n",
    " <th>att1</th>\n",
    " <th>att2</th>\n",
    " </tr>\n",
    " </thead>\n",
    " <tbody>\n",
    " <tr>\n",
    " <th>0</th>\n",
    " <td>a</td>\n",
    " <td>3.856</td>\n",
    " <td>16.8</td>\n",
    " <td>4.838</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>1</th>\n",
    " <td>a</td>\n",
    " <td>7.339</td>\n",
    " <td>16.8</td>\n",
    " <td>6.793</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>2</th>\n",
    " <td>a</td>\n",
    " <td>3.558</td>\n",
    " <td>16.8</td>\n",
    " <td>4.758</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>3</th>\n",
    " <td>a</td>\n",
    " <td>5.304</td>\n",
    " <td>16.8</td>\n",
    " <td>5.351</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>4</th>\n",
    " <td>a</td>\n",
    " <td>7.332</td>\n",
    " <td>16.8</td>\n",
    " <td>3.495</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>...</th>\n",
    " <td>...</td>\n",
    " <td>...</td>\n",
    " <td>...</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>295</th>\n",
    " <td>c</td>\n",
    " <td>4.732</td>\n",
    " <td>15.3</td>\n",
    " <td>2.104</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>296</th>\n",
    " <td>c</td>\n",
    " <td>3.885</td>\n",
    " <td>15.3</td>\n",
    " <td>2.987</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>297</th>\n",
    " <td>c</td>\n",
    " <td>3.680</td>\n",
    " <td>15.3</td>\n",
    " <td>2.647</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>298</th>\n",
    " <td>c</td>\n",
    " <td>2.213</td>\n",
    " <td>15.3</td>\n",
    " <td>2.607</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>299</th>\n",
    " <td>c</td>\n",
    " <td>4.592</td>\n",
    " <td>15.3</td>\n",
    " <td>3.236</td>\n",
    " </tr>\n",
    " </tbody>\n",
    "</table>\n",
    "<p>300 rows × 3 columns</p>\n",
    "<p>300 rows × 2 columns</p>\n",
    "</div>"
    ],
    "text/plain": [
    " name att1 att2\n",
    "0 a 3.856 16.8\n",
    "1 a 7.339 16.8\n",
    "2 a 3.558 16.8\n",
    "3 a 5.304 16.8\n",
    "4 a 7.332 16.8\n",
    ".. ... ... ...\n",
    "295 c 4.732 15.3\n",
    "296 c 3.885 15.3\n",
    "297 c 3.680 15.3\n",
    "298 c 2.213 15.3\n",
    "299 c 4.592 15.3\n",
    " name att1\n",
    "0 a 4.838\n",
    "1 a 6.793\n",
    "2 a 4.758\n",
    "3 a 5.351\n",
    "4 a 3.495\n",
    ".. ... ...\n",
    "295 c 2.104\n",
    "296 c 2.987\n",
    "297 c 2.647\n",
    "298 c 2.607\n",
    "299 c 3.236\n",
    "\n",
    "[300 rows x 3 columns]"
    "[300 rows x 2 columns]"
    ]
    },
    "execution_count": 5,
    "execution_count": 16,
    "metadata": {},
    "output_type": "execute_result"
    }
    @@ -528,17 +516,14 @@
    "]\n",
    "\n",
    "def series_constructor(series_descriptor_list):\n",
    " names, att1s, att2s = [], [], []\n",
    " names, att1s = [], []\n",
    " for sd in series_descriptor_list:\n",
    " names += [sd.name for i in range(sd.num_samples)]\n",
    " # Random number between sd.lower and sd.upper\n",
    " att1s += [np.round(np.random.uniform(sd.lower, sd.upper),3) for i in range(sd.num_samples)]\n",
    " # Another attribute showing use of some numpy capabilities (round, mean, array operations)\n",
    " att2s += [np.round(np.mean(np.array(att1s))*3,1) for i in range(sd.num_samples)]\n",
    " # Create DataFrame with first two columns\n",
    " df = pd.DataFrame({'name': names, 'att1': att1s})\n",
    " # Insert another column (just to demonstrate how to do do)\n",
    " df['att2'] = att2s\n",
    " return df\n",
    "\n",
    "df = series_constructor(my_series_descriptor_list)\n",
  6. joaovicente revised this gist Oct 6, 2020. 1 changed file with 306 additions and 80 deletions.
    386 changes: 306 additions & 80 deletions pandas-101.ipynb
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    306 additions, 80 deletions not shown because the diff is too large. Please use a local Git client to view these changes.
  7. joaovicente created this gist Sep 29, 2020.
    674 changes: 674 additions & 0 deletions pandas-101.ipynb
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    {
    "cells": [
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "# Pandas-101\n",
    "A very hands-on introduction to Pandas and Matplotlib to compare series of data."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import pandas as pd"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "## Creating Pandas"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "text/html": [
    "<div>\n",
    "<style scoped>\n",
    " .dataframe tbody tr th:only-of-type {\n",
    " vertical-align: middle;\n",
    " }\n",
    "\n",
    " .dataframe tbody tr th {\n",
    " vertical-align: top;\n",
    " }\n",
    "\n",
    " .dataframe thead th {\n",
    " text-align: right;\n",
    " }\n",
    "</style>\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    " <thead>\n",
    " <tr style=\"text-align: right;\">\n",
    " <th></th>\n",
    " <th>a</th>\n",
    " <th>b</th>\n",
    " </tr>\n",
    " </thead>\n",
    " <tbody>\n",
    " <tr>\n",
    " <th>0</th>\n",
    " <td>1</td>\n",
    " <td>2</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>1</th>\n",
    " <td>2</td>\n",
    " <td>4</td>\n",
    " </tr>\n",
    " </tbody>\n",
    "</table>\n",
    "</div>"
    ],
    "text/plain": [
    " a b\n",
    "0 1 2\n",
    "1 2 4"
    ]
    },
    "execution_count": 2,
    "metadata": {},
    "output_type": "execute_result"
    }
    ],
    "source": [
    "# Using List of records (array of dictionaries)\n",
    "list_of_dicts = [{'a':1,'b':2},{'a':2, 'b':4}] \n",
    "df = pd.DataFrame(list_of_dicts)\n",
    "df"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "text/html": [
    "<div>\n",
    "<style scoped>\n",
    " .dataframe tbody tr th:only-of-type {\n",
    " vertical-align: middle;\n",
    " }\n",
    "\n",
    " .dataframe tbody tr th {\n",
    " vertical-align: top;\n",
    " }\n",
    "\n",
    " .dataframe thead th {\n",
    " text-align: right;\n",
    " }\n",
    "</style>\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    " <thead>\n",
    " <tr style=\"text-align: right;\">\n",
    " <th></th>\n",
    " <th>a</th>\n",
    " <th>b</th>\n",
    " </tr>\n",
    " </thead>\n",
    " <tbody>\n",
    " <tr>\n",
    " <th>0</th>\n",
    " <td>1</td>\n",
    " <td>2</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>1</th>\n",
    " <td>2</td>\n",
    " <td>4</td>\n",
    " </tr>\n",
    " </tbody>\n",
    "</table>\n",
    "</div>"
    ],
    "text/plain": [
    " a b\n",
    "0 1 2\n",
    "1 2 4"
    ]
    },
    "execution_count": 3,
    "metadata": {},
    "output_type": "execute_result"
    }
    ],
    "source": [
    "# Using List of column values\n",
    "a = [1,2]\n",
    "b = [2,4]\n",
    "df = pd.DataFrame({'a':a, 'b':b})\n",
    "df"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "## Create 3 series of data to compare with each other later"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "text/html": [
    "<div>\n",
    "<style scoped>\n",
    " .dataframe tbody tr th:only-of-type {\n",
    " vertical-align: middle;\n",
    " }\n",
    "\n",
    " .dataframe tbody tr th {\n",
    " vertical-align: top;\n",
    " }\n",
    "\n",
    " .dataframe thead th {\n",
    " text-align: right;\n",
    " }\n",
    "</style>\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    " <thead>\n",
    " <tr style=\"text-align: right;\">\n",
    " <th></th>\n",
    " <th>name</th>\n",
    " <th>att1</th>\n",
    " <th>att2</th>\n",
    " </tr>\n",
    " </thead>\n",
    " <tbody>\n",
    " <tr>\n",
    " <th>0</th>\n",
    " <td>a</td>\n",
    " <td>7.720</td>\n",
    " <td>17.0</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>1</th>\n",
    " <td>a</td>\n",
    " <td>4.488</td>\n",
    " <td>17.0</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>2</th>\n",
    " <td>a</td>\n",
    " <td>6.082</td>\n",
    " <td>17.0</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>3</th>\n",
    " <td>a</td>\n",
    " <td>7.346</td>\n",
    " <td>17.0</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>4</th>\n",
    " <td>a</td>\n",
    " <td>6.603</td>\n",
    " <td>17.0</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>...</th>\n",
    " <td>...</td>\n",
    " <td>...</td>\n",
    " <td>...</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>295</th>\n",
    " <td>c</td>\n",
    " <td>4.526</td>\n",
    " <td>15.6</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>296</th>\n",
    " <td>c</td>\n",
    " <td>3.590</td>\n",
    " <td>15.6</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>297</th>\n",
    " <td>c</td>\n",
    " <td>3.688</td>\n",
    " <td>15.6</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>298</th>\n",
    " <td>c</td>\n",
    " <td>4.356</td>\n",
    " <td>15.6</td>\n",
    " </tr>\n",
    " <tr>\n",
    " <th>299</th>\n",
    " <td>c</td>\n",
    " <td>2.546</td>\n",
    " <td>15.6</td>\n",
    " </tr>\n",
    " </tbody>\n",
    "</table>\n",
    "<p>300 rows × 3 columns</p>\n",
    "</div>"
    ],
    "text/plain": [
    " name att1 att2\n",
    "0 a 7.720 17.0\n",
    "1 a 4.488 17.0\n",
    "2 a 6.082 17.0\n",
    "3 a 7.346 17.0\n",
    "4 a 6.603 17.0\n",
    ".. ... ... ...\n",
    "295 c 4.526 15.6\n",
    "296 c 3.590 15.6\n",
    "297 c 3.688 15.6\n",
    "298 c 4.356 15.6\n",
    "299 c 2.546 15.6\n",
    "\n",
    "[300 rows x 3 columns]"
    ]
    },
    "execution_count": 4,
    "metadata": {},
    "output_type": "execute_result"
    }
    ],
    "source": [
    "# Create 3 series of samples\n",
    "num_samples = 100\n",
    "class SeriesDescriptor:\n",
    " def __init__(self, name, lower, upper, num_samples):\n",
    " self.name = name\n",
    " self.lower = lower\n",
    " self.upper = upper\n",
    " self.num_samples = num_samples\n",
    "\n",
    "my_series_descriptor_list = [\n",
    " SeriesDescriptor('a',3,8,num_samples), \n",
    " SeriesDescriptor('b',4,9,num_samples), \n",
    " SeriesDescriptor('c',2,5,num_samples)\n",
    "]\n",
    "\n",
    "def series_constructor(series_descriptor_list):\n",
    " names, att1s, att2s = [], [], []\n",
    " for sd in series_descriptor_list:\n",
    " names += [sd.name for i in range(sd.num_samples)]\n",
    " # Random number between sd.lower and sd.upper\n",
    " att1s += [np.round(np.random.uniform(sd.lower, sd.upper),3) for i in range(sd.num_samples)]\n",
    " # Another attribute showing use of some numpy capabilities (round, mean, array operations)\n",
    " att2s += [np.round(np.mean(np.array(att1s))*3,1) for i in range(sd.num_samples)]\n",
    " # Create Panda DataFrame with first two columns\n",
    " df = pd.DataFrame({'name': names, 'att1': att1s})\n",
    " # Insert another column (just to demonstrate how to do do)\n",
    " df['att2'] = att2s\n",
    " return df\n",
    "\n",
    "df = series_constructor(my_series_descriptor_list)\n",
    "# Inspect data frame\n",
    "df"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
    "# Write Data Frame to CSV\n",
    "df.to_csv('randomSamples.csv', encoding='utf-8', index=False)"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
    "# Read Data Frame from CSV\n",
    "df1 = pd.DataFrame(pd.read_csv('randomSamples.csv'))"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "['a', 'b', 'c']\n"
    ]
    }
    ],
    "source": [
    "# Rename column from att1 to attr1\n",
    "df1.rename(columns = {'att1': 'attr1'}, inplace = True)\n",
    "\n",
    "# Select desired columns (dropping att2)\n",
    "df2 = df1[['name','attr1']]\n",
    "\n",
    "# Remove 'name:c' samples\n",
    "remove = False\n",
    "if remove:\n",
    " df3 = df2[df2.name != 'c']\n",
    "else:\n",
    " df3 = df2\n",
    "\n",
    "# Group data frame by 'name' column\n",
    "dfg = df3.groupby(df3.name)\n",
    "keys = list(dfg.groups.keys())\n",
    "print(keys)"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "\n",
    "a\n",
    "count 100.000000\n",
    "mean 5.670590\n",
    "std 1.335251\n",
    "min 3.001000\n",
    "25% 4.589750\n",
    "50% 5.691500\n",
    "75% 6.813000\n",
    "max 7.813000\n",
    "Name: attr1, dtype: float64\n",
    "\n",
    "b\n",
    "count 100.000000\n",
    "mean 6.396540\n",
    "std 1.448266\n",
    "min 4.017000\n",
    "25% 5.131500\n",
    "50% 6.304000\n",
    "75% 7.559750\n",
    "max 8.999000\n",
    "Name: attr1, dtype: float64\n",
    "\n",
    "c\n",
    "count 100.000000\n",
    "mean 3.531500\n",
    "std 0.960461\n",
    "min 2.018000\n",
    "25% 2.611000\n",
    "50% 3.657000\n",
    "75% 4.446000\n",
    "max 4.995000\n",
    "Name: attr1, dtype: float64\n"
    ]
    }
    ],
    "source": [
    "# Show statistics for each queryName engineExecutionTimeInMillis\n",
    "for key in keys:\n",
    " print(\"\\n\" + key)\n",
    " print(dfg.get_group(key)['attr1'].describe())"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "text/plain": [
    "a AxesSubplot(0.1,0.15;0.235294x0.75)\n",
    "b AxesSubplot(0.382353,0.15;0.235294x0.75)\n",
    "c AxesSubplot(0.664706,0.15;0.235294x0.75)\n",
    "dtype: object"
    ]
    },
    "execution_count": 9,
    "metadata": {},
    "output_type": "execute_result"
    },
    {
    "data": {
    "image/png": "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\n",
    "text/plain": [
    "<Figure size 432x288 with 3 Axes>"
    ]
    },
    "metadata": {
    "needs_background": "light"
    },
    "output_type": "display_data"
    }
    ],
    "source": [
    "# Quick boxplot\n",
    "dfg.boxplot(layout=(1,len(keys)))"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "image/png": 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+iLYkSf3r95TUVe3TwEVV9btJXkrvttySpI4NPRSSbAjsA7wHoKqe4rmP+pQkdWQ2D9lZVbYFlgNfSHJtkpOnuutqkoVJliRZsnz58uFXKUlroC5CYR6wG/C5qno9vSukF01eqaoWV9V4VY2PjY0Nu0ZJWiN1EQpLgaVVdUUzfy69kJAkdWzooVBV9wF3JdmhWbQ/cNOw65AkPV9XZx+9HzizOfPoJ8BRHdUhSZqgk1CoquuA8S7aliRNr4tjCpKkEWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqdXVxWsagAWLLuy6BEkvcvYUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEmtzkIhyVpJrk3yza5qkCQ9V5c9hWOBmztsX5I0SSehkGRr4EDg5C7alyRNrauewqeA44BfTrdCkoVJliRZsnz58qEVJklrsqGHQpKDgGVVdfXK1quqxVU1XlXjY2NjQ6pOktZsXfQU9gIOTnI78GVgvyRndFCHJGmSoYdCVR1fVVtX1QLgMOC7VXXEsOuQJD2f1ylIklqdPnmtqr4PfL/LGiRJ/8aegiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpNfRQSLJNku8luSnJjUmOHXYNkqSpdfE4zmeAD1XVNUk2AK5OcmlV3dRBLZKkCYbeU6iqe6vqmmb6EeBmYKth1yFJer5OjykkWQC8HrhiitcWJlmSZMny5cuHXpskrYk6C4Uk6wNfBT5QVQ9Pfr2qFlfVeFWNj42NDb9ASVoDdRIKSdamFwhnVtV5XdQgSXq+Ls4+CnAKcHNVfWLY7UuSptdFT2Ev4PeB/ZJc1/z8Zgd1SJImGfopqVX1T0CG3a4kaWZe0SxJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJanUSCkkOSHJrkn9JsqiLGiRJzzf0UEiyFvBZ4B3ATsDhSXYadh2SpOfroqewB/AvVfWTqnoK+DJwSAd1SJImmddBm1sBd02YXwq8cfJKSRYCC5vZJ5PcMITaZmMz4IGui5hkFGuC0azLmvrTSU356EpfHsXPCUazrh1mu0EXodCXqloMLAZIsqSqxjsu6TmsqX+jWJc19cea+jeKdSVZMtttuhg+uhvYZsL81s0ySVLHugiFq4Dtkmyb5KXAYcDXO6hDkjTJ0IePquqZJH8MXAysBZxaVTfOsNniwVc2a9bUv1Gsy5r6Y039G8W6Zl1TqmoQhUiSXoS8olmS1DIUJEmtkQ6FJKcmWTZK1ygk2SbJ95LclOTGJMeOQE3rJLkyyQ+bmv6865pWSLJWkmuTfLPrWgCS3J7k+iTXzeV0vUFJslGSc5PckuTmJL/ecT07NJ/Rip+Hk3ygy5qauj7Y/Bu/IclZSdYZgZqObeq5scvPaKrvyySbJLk0yW3N741n2s9IhwJwGnBA10VM8gzwoaraCdgTOGYEbtPxJLBfVe0KvA44IMme3ZbUOha4uesiJnlLVb1uxM4p/zRwUVXtCOxKx59ZVd3afEavA94APA6c32VNSbYC/gQYr6qd6Z2ocljHNe0MvI/enRp2BQ5K8msdlXMaz/++XAR8p6q2A77TzK/USIdCVV0GPNh1HRNV1b1VdU0z/Qi9/7xbdVxTVdWjzezazU/nZxAk2Ro4EDi561pGWZINgX2AUwCq6qmqeqjTop5rf+DHVXVH14XQO2PyZUnmAesC93Rcz2uAK6rq8ap6BvgB8NtdFDLN9+UhwOnN9OnAu2baz0iHwqhLsgB4PXBFx6WsGKa5DlgGXFpVndcEfAo4Dvhlx3VMVMAlSa5ubqUyCrYFlgNfaIbaTk6yXtdFTXAYcFbXRVTV3cDHgDuBe4GfV9Ul3VbFDcDeSTZNsi7wmzz34tyubV5V9zbT9wGbz7SBoTBHSdYHvgp8oKoe7rqeqnq26epvDezRdGs7k+QgYFlVXd1lHVP491W1G7279B6TZJ+uC6L31+9uwOeq6vXAY/TRzR+G5gLTg4FzRqCWjen95bstsCWwXpIjuqypqm4GPgpcAlwEXAc822VN06ne9QczjiAYCnOQZG16gXBmVZ3XdT0TNcMO36P7YzF7AQcnuZ3enXD3S3JGtyW1f21SVcvojZHv0W1FQO+mkEsn9O7OpRcSo+AdwDVVdX/XhQBvBX5aVcur6mngPOBNHddEVZ1SVW+oqn2AfwV+1HVNE9yfZAuA5veymTYwFGYpSeiN/d5cVZ/ouh6AJGNJNmqmXwa8Dbily5qq6viq2rqqFtAbfvhuVXX6V12S9ZJssGIaeDu97n+nquo+4K4kK+5ouT9wU4clTXQ4IzB01LgT2DPJus3/w/0ZgZMYkryi+T2f3vGEL3Vb0XN8HTiymT4S+NpMG4zsXVIBkpwF7AtslmQpcEJVndJtVewF/D5wfTOGD/CRqvpWdyWxBXB68wCjlwBnV9VInAI6YjYHzu99nzAP+FJVXdRtSa33A2c2wzU/AY7quJ4Vwfk24A+6rgWgqq5Ici5wDb2zAK9lNG4t8dUkmwJPA8d0dZLAVN+XwEnA2UmOBu4ADp1xP97mQpK0gsNHkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqTW/wd2GkHZBh1Q8wAAAABJRU5ErkJggg==\n",
    "text/plain": [
    "<Figure size 432x288 with 1 Axes>"
    ]
    },
    "metadata": {
    "needs_background": "light"
    },
    "output_type": "display_data"
    },
    {
    "data": {
    "image/png": 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\n",
    "text/plain": [
    "<Figure size 432x288 with 1 Axes>"
    ]
    },
    "metadata": {
    "needs_background": "light"
    },
    "output_type": "display_data"
    },
    {
    "data": {
    "image/png": "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\n",
    "text/plain": [
    "<Figure size 432x288 with 1 Axes>"
    ]
    },
    "metadata": {
    "needs_background": "light"
    },
    "output_type": "display_data"
    }
    ],
    "source": [
    "# Split histogram\n",
    "lower_limit = np.round(df3['attr1'].min()-1)\n",
    "higher_limit = np.round(df3['attr1'].max()+1)\n",
    "for i in dfg.groups.keys():\n",
    " dfg.get_group(i)['attr1'].plot(kind='hist')\n",
    " plt.title(f\"{i} attr1 histogram\")\n",
    " plt.xlim(lower_limit,higher_limit)\n",
    " plt.show()"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "text/plain": [
    "Text(0.5, 1.0, 'Combined histogram')"
    ]
    },
    "execution_count": 11,
    "metadata": {},
    "output_type": "execute_result"
    },
    {
    "data": {
    "image/png": 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\n",
    "text/plain": [
    "<Figure size 432x288 with 1 Axes>"
    ]
    },
    "metadata": {
    "needs_background": "light"
    },
    "output_type": "display_data"
    }
    ],
    "source": [
    "# Joined histogram\n",
    "dfg['attr1'].plot(kind='hist', alpha=0.3)\n",
    "plt.legend()\n",
    "plt.title(\"Combined histogram\")"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "text/plain": [
    "Text(0, 0.5, 'Attr1')"
    ]
    },
    "execution_count": 12,
    "metadata": {},
    "output_type": "execute_result"
    },
    {
    "data": {
    "image/png": 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\n",
    "text/plain": [
    "<Figure size 432x288 with 1 Axes>"
    ]
    },
    "metadata": {
    "needs_background": "light"
    },
    "output_type": "display_data"
    }
    ],
    "source": [
    "# A scatter plot with jitter\n",
    "ax = plt.figure().gca()\n",
    "ax.xaxis.set_ticklabels([])\n",
    "ax.xaxis.set_visible(False)\n",
    "plt.xlim(0, len(keys) + 1)\n",
    "size = 6\n",
    "\n",
    "# Order series by attr1 mean\n",
    "mean_ordered_keys = list(dfg.mean().sort_values('attr1', ascending=True).index)\n",
    "\n",
    "x = 1 \n",
    "for key in mean_ordered_keys:\n",
    " serie = dfg.get_group(key)['attr1'].tolist()\n",
    " serie_jitter = [np.random.uniform(x-0.2,x+0.2) for i in serie]\n",
    " plt.scatter(serie_jitter, serie, label=key, s=size, alpha=0.7)\n",
    " x += 1\n",
    " \n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Scatter plot with jitter\")\n",
    "plt.ylabel(\"Attr1\", size=14)"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
    "outputs": [
    {
    "data": {
    "text/plain": [
    "Text(0.5, 1.0, 'Mean att by name')"
    ]
    },
    "execution_count": 13,
    "metadata": {},
    "output_type": "execute_result"
    },
    {
    "data": {
    "image/png": 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\n",
    "text/plain": [
    "<Figure size 432x288 with 1 Axes>"
    ]
    },
    "metadata": {
    "needs_background": "light"
    },
    "output_type": "display_data"
    }
    ],
    "source": [
    "# A bar chart to compare column means \n",
    "df.groupby(df.name).mean().plot(kind='bar')\n",
    "plt.legend()\n",
    "plt.title(\"Mean att by name\")"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "## References and further reading\n",
    "\n",
    "* [pandas.pydata.org](https://pandas.pydata.org/)\n",
    "* [matplotlib.org](https://matplotlib.org/)\n",
    "* [Using Pandas DataFrame guide by Shane Lynn](https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/)\n",
    "* [Don't compare averages by Martin Fowler](https://martinfowler.com/articles/dont-compare-averages.html)\n",
    "* [Choosing a good chart by Extreme Presentation](https://extremepresentation.com/wp-content/uploads/choosing-a-good-chart-09-1.pdf)"
    ]
    }
    ],
    "metadata": {
    "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": "3.8.5"
    }
    },
    "nbformat": 4,
    "nbformat_minor": 4
    }