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seanturner026/Blog+Post+2+Code-Copy1.ipynb

Created August 7, 2017 04:01
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Blog+Post+2+Code-Copy1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas and Useful Techniques"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In week two of General Assembly, the cohort spent a lot of time covering: \n",
"\n",
"- Pandas\n",
"- Data Visualization Techniques\n",
"- sqlite3 with Python and Pandas\n",
"- postgreSQL with Python and Pandas\n",
"\n",
"Instead of doing a full weekly recap of what the corhort covered in week 2, I believe it would be better to instead go through the useful things I picked up on my own using what I learned while working through practice problems."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline\n",
"sns.set()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Seaborn comes with some datasets, see https://github.com/mwaskom/seaborn-data for a comlete list\n",
"iris = sns.load_dataset('iris')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Firstly, I figured out a way to print a DataFrame's head and tail at the same time. While print( df.head() , df.tail() ) is an option, I prefer np.r_ because the DataFrame's appearance is maintained. \n",
"\n",
"np.r_ is useful when trying to construct arrays quickly. Feeding the function slices of the first and last five elements of a sequence generates an array that can be passed to pandas iloc function to print the head and tail in one line.\n",
"\n",
"While DataFrame.head is firmly ingrained in muscle memory at this point, I think making a habit of np.r_ will be a worthwile effort."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, -5, -4, -3, -2, -1])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.r_[0:5, -5:0]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa\n",
"145 6.7 3.0 5.2 2.3 virginica\n",
"146 6.3 2.5 5.0 1.9 virginica\n",
"147 6.5 3.0 5.2 2.0 virginica\n",
"148 6.2 3.4 5.4 2.3 virginica\n",
"149 5.9 3.0 5.1 1.8 virginica"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.iloc[np.r_[0:5, -5:0]]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"( sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa, sepal_length sepal_width petal_length petal_width species\n",
"145 6.7 3.0 5.2 2.3 virginica\n",
"146 6.3 2.5 5.0 1.9 virginica\n",
"147 6.5 3.0 5.2 2.0 virginica\n",
"148 6.2 3.4 5.4 2.3 virginica\n",
"149 5.9 3.0 5.1 1.8 virginica)\n"
]
}
],
"source": [
"#more difficult to read, especially given a DataFrame with many columns\n",
"print(iris.head(), iris.tail())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------------------------------------------------------------------------------------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After some time spent gooogling and reading matplotlib documentation, I also figured out a way to generate multiple plots in one cell with loops and matplotlib's subplot function. I am pround of this discovery because plotting is incredibly useful, and plotting the four violinplots from below would have required cut and pasting. plt.subplot also minimizes notebook clutter and scrolling.\n",
"\n",
"In the below, I loop through the columns in the iris dataset (excluding species), and plot each column to a violin plot by species."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = iris.columns.values[:-1]\n",
"cols"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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Nzz+/gMWLF37IowamNwt3dgmWvwArvfC8O/C5dNRtJdpSfekF7WFOJMj57tyc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"text/plain": [
"<matplotlib.figure.Figure at 0x111382590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = [15,12])\n",
"\n",
"# The below output is generated by creating a plot with plt.subplot in position i.\n",
"# i is determined by enumerate, and starts at 1 because subplot doesn't recognize zero as a position.\n",
"for i, col in enumerate(cols, start = 1):\n",
" \n",
" # Could also use if statements and modulo to check if i is even, in order to alternate plot type and more\n",
" # plt.subplot(1,4,i) would create a figure with one row and four columns.\n",
" plt.subplot(2,2,i), sns.violinplot(x = col, y = 'species', data = iris)\n",
" plt.xlabel(col.replace('_',' '))\n",
" plt.title('Subplot ' + str(i))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I also played around with masking."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>95</td>\n",
" <td>76</td>\n",
" <td>24</td>\n",
" <td>83</td>\n",
" <td>71</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11</td>\n",
" <td>95</td>\n",
" <td>36</td>\n",
" <td>84</td>\n",
" <td>20</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>81</td>\n",
" <td>87</td>\n",
" <td>63</td>\n",
" <td>47</td>\n",
" <td>27</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>14</td>\n",
" <td>75</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>63</td>\n",
" <td>73</td>\n",
" <td>35</td>\n",
" <td>69</td>\n",
" <td>5</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>34</td>\n",
" <td>17</td>\n",
" <td>10</td>\n",
" <td>21</td>\n",
" <td>47</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>44</td>\n",
" <td>75</td>\n",
" <td>94</td>\n",
" <td>21</td>\n",
" <td>29</td>\n",
" <td>71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>72</td>\n",
" <td>79</td>\n",
" <td>35</td>\n",
" <td>81</td>\n",
" <td>60</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>19</td>\n",
" <td>97</td>\n",
" <td>28</td>\n",
" <td>23</td>\n",
" <td>53</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>10</td>\n",
" <td>93</td>\n",
" <td>3</td>\n",
" <td>94</td>\n",
" <td>11</td>\n",
" <td>62</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E F\n",
"0 95 76 24 83 71 40\n",
"1 11 95 36 84 20 91\n",
"2 81 87 63 47 27 45\n",
"3 70 0 19 14 75 97\n",
"4 63 73 35 69 5 24\n",
"25 34 17 10 21 47 70\n",
"26 44 75 94 21 29 71\n",
"27 72 79 35 81 60 84\n",
"28 19 97 28 23 53 10\n",
"29 10 93 3 94 11 62"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using numpy to create a DataFrame\n",
"np.random.seed(101)\n",
"\n",
"columns = 'A B C D E F'.split(' ')\n",
"rows = [np.random.randint(low = 0, high = 100, size = 30) for x in range(6)]\n",
"\n",
"df = pd.DataFrame(dict(zip(columns, rows)))\n",
"\n",
"df.iloc[np.r_[0:5, -5:0]] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Masking is the use of conditional selection to modify **specific** items in specified columns. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>unknown</td>\n",
" <td>76</td>\n",
" <td>24</td>\n",
" <td>83</td>\n",
" <td>71</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11</td>\n",
" <td>95</td>\n",
" <td>36</td>\n",
" <td>84</td>\n",
" <td>20</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>81</td>\n",
" <td>87</td>\n",
" <td>63</td>\n",
" <td>strings</td>\n",
" <td>strings</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>unknown</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>14</td>\n",
" <td>75</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>63</td>\n",
" <td>73</td>\n",
" <td>35</td>\n",
" <td>string2</td>\n",
" <td>5</td>\n",
" <td>24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E F\n",
"0 unknown 76 24 83 71 40\n",
"1 11 95 36 84 20 91\n",
"2 81 87 63 strings strings 45\n",
"3 unknown 0 19 14 75 97\n",
"4 63 73 35 string2 5 24"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# can use conditional selection to alter values in one column at a time\n",
"df.loc[df['B'].isin([0, 76]) , 'A'] = 'unknown'\n",
"\n",
"# or values in multiple columns\n",
"df.loc[df['F'] == 45, ['D', 'E']] = 'strings'\n",
"\n",
"# or with multiple conditions\n",
"df.loc[(df['B'] == 73) & (df['E'] == 5), 'D'] = 'string2'\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 30 entries, 0 to 29\n",
"Data columns (total 6 columns):\n",
"A 30 non-null object\n",
"B 30 non-null int64\n",
"C 30 non-null int64\n",
"D 30 non-null object\n",
"E 30 non-null object\n",
"F 30 non-null int64\n",
"dtypes: int64(3), object(3)\n",
"memory usage: 1.5+ KB\n"
]
}
],
"source": [
"# The datatype of columns A, D, E was converted from integers to objects, which do not understand math operations\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" text-align: left;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>76</td>\n",
" <td>24</td>\n",
" <td>83</td>\n",
" <td>71</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11.0</td>\n",
" <td>95</td>\n",
" <td>36</td>\n",
" <td>84</td>\n",
" <td>20</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>81.0</td>\n",
" <td>87</td>\n",
" <td>63</td>\n",
" <td>strings</td>\n",
" <td>strings</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>14</td>\n",
" <td>75</td>\n",
" <td>97</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E F\n",
"0 NaN 76 24 83 71 40\n",
"1 11.0 95 36 84 20 91\n",
"2 81.0 87 63 strings strings 45\n",
"3 NaN 0 19 14 75 97"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pd.to_numeric converts column to float64 by converting all non-numerics to null values\n",
"df['A'] = pd.to_numeric(df['A'], errors = 'coerce')\n",
"df.head(4)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 30 entries, 0 to 29\n",
"Data columns (total 6 columns):\n",
"A 27 non-null float64\n",
"B 30 non-null int64\n",
"C 30 non-null int64\n",
"D 30 non-null object\n",
"E 30 non-null object\n",
"F 30 non-null int64\n",
"dtypes: float64(1), int64(3), object(2)\n",
"memory usage: 1.5+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A 3\n",
"B 0\n",
"C 0\n",
"D 0\n",
"E 0\n",
"F 0\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Count of null values (NaN) per column\n",
"df.isnull().sum()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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