{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The Iris dataset and pandas\n", "\n", "![pandas logo](https://upload.wikimedia.org/wikipedia/commons/thumb/e/ed/Pandas_logo.svg/640px-Pandas_logo.svg.png)\n", "\n", "![Iris flowers](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Machine+Learning+R/iris-machinelearning.png)\n", "\n", "***\n", "\n", "**[Python Data Analysis Library](https://pandas.pydata.org/)**\n", "\n", "*[https://pandas.pydata.org/](https://pandas.pydata.org/)*\n", "\n", "The pandas website.\n", "\n", "***\n", "\n", "**[Wes McKinney: pandas in 10 minutes | Walkthrough](https://www.youtube.com/watch?foo=bar&v=_T8LGqJtuGc?)**\n", "\n", "*[https://www.youtube.com/watch?v=_T8LGqJtuGc](https://www.youtube.com/watch?foo=bar&v=_T8LGqJtuGc)*\n", "\n", "Video by the creator of pandas.\n", "\n", "***\n", "\n", "**[Python for Data Analysis notebooks](https://github.com/wesm/pydata-book)**\n", "\n", "*[https://github.com/wesm/pydata-book](https://github.com/wesm/pydata-book)*\n", "\n", "Materials and IPython notebooks for \"Python for Data Analysis\" by Wes McKinney, published by O'Reilly Media\n", "\n", "***\n", "\n", "**[10 Minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html)**\n", "\n", "*[http://pandas.pydata.org/pandas-docs/stable/10min.html](http://pandas.pydata.org/pandas-docs/stable/10min.html)*\n", "\n", "Official pandas tutorial.\n", "\n", "***\n", "\n", "**[UC Irvine Machine Learning Repository: Iris Data Set](https://archive.ics.uci.edu/ml/datasets/iris)**\n", "\n", "*[https://archive.ics.uci.edu/ml/datasets/iris](https://archive.ics.uci.edu/ml/datasets/iris)*\n", "\n", "About the Iris data set from UC Irvine's machine learning repository." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Import pandas.\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Load the iris data set from a URL.\n", "df = pd.read_csv(\"https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
55.43.91.70.4setosa
64.63.41.40.3setosa
75.03.41.50.2setosa
84.42.91.40.2setosa
94.93.11.50.1setosa
105.43.71.50.2setosa
114.83.41.60.2setosa
124.83.01.40.1setosa
134.33.01.10.1setosa
145.84.01.20.2setosa
155.74.41.50.4setosa
165.43.91.30.4setosa
175.13.51.40.3setosa
185.73.81.70.3setosa
195.13.81.50.3setosa
205.43.41.70.2setosa
215.13.71.50.4setosa
224.63.61.00.2setosa
235.13.31.70.5setosa
244.83.41.90.2setosa
255.03.01.60.2setosa
265.03.41.60.4setosa
275.23.51.50.2setosa
285.23.41.40.2setosa
294.73.21.60.2setosa
..................
1206.93.25.72.3virginica
1215.62.84.92.0virginica
1227.72.86.72.0virginica
1236.32.74.91.8virginica
1246.73.35.72.1virginica
1257.23.26.01.8virginica
1266.22.84.81.8virginica
1276.13.04.91.8virginica
1286.42.85.62.1virginica
1297.23.05.81.6virginica
1307.42.86.11.9virginica
1317.93.86.42.0virginica
1326.42.85.62.2virginica
1336.32.85.11.5virginica
1346.12.65.61.4virginica
1357.73.06.12.3virginica
1366.33.45.62.4virginica
1376.43.15.51.8virginica
1386.03.04.81.8virginica
1396.93.15.42.1virginica
1406.73.15.62.4virginica
1416.93.15.12.3virginica
1425.82.75.11.9virginica
1436.83.25.92.3virginica
1446.73.35.72.5virginica
1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3virginica
1495.93.05.11.8virginica
\n", "

150 rows × 5 columns

\n", "
" ], "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", "5 5.4 3.9 1.7 0.4 setosa\n", "6 4.6 3.4 1.4 0.3 setosa\n", "7 5.0 3.4 1.5 0.2 setosa\n", "8 4.4 2.9 1.4 0.2 setosa\n", "9 4.9 3.1 1.5 0.1 setosa\n", "10 5.4 3.7 1.5 0.2 setosa\n", "11 4.8 3.4 1.6 0.2 setosa\n", "12 4.8 3.0 1.4 0.1 setosa\n", "13 4.3 3.0 1.1 0.1 setosa\n", "14 5.8 4.0 1.2 0.2 setosa\n", "15 5.7 4.4 1.5 0.4 setosa\n", "16 5.4 3.9 1.3 0.4 setosa\n", "17 5.1 3.5 1.4 0.3 setosa\n", "18 5.7 3.8 1.7 0.3 setosa\n", "19 5.1 3.8 1.5 0.3 setosa\n", "20 5.4 3.4 1.7 0.2 setosa\n", "21 5.1 3.7 1.5 0.4 setosa\n", "22 4.6 3.6 1.0 0.2 setosa\n", "23 5.1 3.3 1.7 0.5 setosa\n", "24 4.8 3.4 1.9 0.2 setosa\n", "25 5.0 3.0 1.6 0.2 setosa\n", "26 5.0 3.4 1.6 0.4 setosa\n", "27 5.2 3.5 1.5 0.2 setosa\n", "28 5.2 3.4 1.4 0.2 setosa\n", "29 4.7 3.2 1.6 0.2 setosa\n", ".. ... ... ... ... ...\n", "120 6.9 3.2 5.7 2.3 virginica\n", "121 5.6 2.8 4.9 2.0 virginica\n", "122 7.7 2.8 6.7 2.0 virginica\n", "123 6.3 2.7 4.9 1.8 virginica\n", "124 6.7 3.3 5.7 2.1 virginica\n", "125 7.2 3.2 6.0 1.8 virginica\n", "126 6.2 2.8 4.8 1.8 virginica\n", "127 6.1 3.0 4.9 1.8 virginica\n", "128 6.4 2.8 5.6 2.1 virginica\n", "129 7.2 3.0 5.8 1.6 virginica\n", "130 7.4 2.8 6.1 1.9 virginica\n", "131 7.9 3.8 6.4 2.0 virginica\n", "132 6.4 2.8 5.6 2.2 virginica\n", "133 6.3 2.8 5.1 1.5 virginica\n", "134 6.1 2.6 5.6 1.4 virginica\n", "135 7.7 3.0 6.1 2.3 virginica\n", "136 6.3 3.4 5.6 2.4 virginica\n", "137 6.4 3.1 5.5 1.8 virginica\n", "138 6.0 3.0 4.8 1.8 virginica\n", "139 6.9 3.1 5.4 2.1 virginica\n", "140 6.7 3.1 5.6 2.4 virginica\n", "141 6.9 3.1 5.1 2.3 virginica\n", "142 5.8 2.7 5.1 1.9 virginica\n", "143 6.8 3.2 5.9 2.3 virginica\n", "144 6.7 3.3 5.7 2.5 virginica\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", "\n", "[150 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## Selecting rows and columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 setosa\n", "1 setosa\n", "2 setosa\n", "3 setosa\n", "4 setosa\n", "5 setosa\n", "6 setosa\n", "7 setosa\n", "8 setosa\n", "9 setosa\n", "10 setosa\n", "11 setosa\n", "12 setosa\n", "13 setosa\n", "14 setosa\n", "15 setosa\n", "16 setosa\n", "17 setosa\n", "18 setosa\n", "19 setosa\n", "20 setosa\n", "21 setosa\n", "22 setosa\n", "23 setosa\n", "24 setosa\n", "25 setosa\n", "26 setosa\n", "27 setosa\n", "28 setosa\n", "29 setosa\n", " ... \n", "120 virginica\n", "121 virginica\n", "122 virginica\n", "123 virginica\n", "124 virginica\n", "125 virginica\n", "126 virginica\n", "127 virginica\n", "128 virginica\n", "129 virginica\n", "130 virginica\n", "131 virginica\n", "132 virginica\n", "133 virginica\n", "134 virginica\n", "135 virginica\n", "136 virginica\n", "137 virginica\n", "138 virginica\n", "139 virginica\n", "140 virginica\n", "141 virginica\n", "142 virginica\n", "143 virginica\n", "144 virginica\n", "145 virginica\n", "146 virginica\n", "147 virginica\n", "148 virginica\n", "149 virginica\n", "Name: species, Length: 150, dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['species']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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petal_lengthspecies
01.4setosa
11.4setosa
21.3setosa
31.5setosa
41.4setosa
51.7setosa
61.4setosa
71.5setosa
81.4setosa
91.5setosa
101.5setosa
111.6setosa
121.4setosa
131.1setosa
141.2setosa
151.5setosa
161.3setosa
171.4setosa
181.7setosa
191.5setosa
201.7setosa
211.5setosa
221.0setosa
231.7setosa
241.9setosa
251.6setosa
261.6setosa
271.5setosa
281.4setosa
291.6setosa
.........
1205.7virginica
1214.9virginica
1226.7virginica
1234.9virginica
1245.7virginica
1256.0virginica
1264.8virginica
1274.9virginica
1285.6virginica
1295.8virginica
1306.1virginica
1316.4virginica
1325.6virginica
1335.1virginica
1345.6virginica
1356.1virginica
1365.6virginica
1375.5virginica
1384.8virginica
1395.4virginica
1405.6virginica
1415.1virginica
1425.1virginica
1435.9virginica
1445.7virginica
1455.2virginica
1465.0virginica
1475.2virginica
1485.4virginica
1495.1virginica
\n", "

150 rows × 2 columns

\n", "
" ], "text/plain": [ " petal_length species\n", "0 1.4 setosa\n", "1 1.4 setosa\n", "2 1.3 setosa\n", "3 1.5 setosa\n", "4 1.4 setosa\n", "5 1.7 setosa\n", "6 1.4 setosa\n", "7 1.5 setosa\n", "8 1.4 setosa\n", "9 1.5 setosa\n", "10 1.5 setosa\n", "11 1.6 setosa\n", "12 1.4 setosa\n", "13 1.1 setosa\n", "14 1.2 setosa\n", "15 1.5 setosa\n", "16 1.3 setosa\n", "17 1.4 setosa\n", "18 1.7 setosa\n", "19 1.5 setosa\n", "20 1.7 setosa\n", "21 1.5 setosa\n", "22 1.0 setosa\n", "23 1.7 setosa\n", "24 1.9 setosa\n", "25 1.6 setosa\n", "26 1.6 setosa\n", "27 1.5 setosa\n", "28 1.4 setosa\n", "29 1.6 setosa\n", ".. ... ...\n", "120 5.7 virginica\n", "121 4.9 virginica\n", "122 6.7 virginica\n", "123 4.9 virginica\n", "124 5.7 virginica\n", "125 6.0 virginica\n", "126 4.8 virginica\n", "127 4.9 virginica\n", "128 5.6 virginica\n", "129 5.8 virginica\n", "130 6.1 virginica\n", "131 6.4 virginica\n", "132 5.6 virginica\n", "133 5.1 virginica\n", "134 5.6 virginica\n", "135 6.1 virginica\n", "136 5.6 virginica\n", "137 5.5 virginica\n", "138 4.8 virginica\n", "139 5.4 virginica\n", "140 5.6 virginica\n", "141 5.1 virginica\n", "142 5.1 virginica\n", "143 5.9 virginica\n", "144 5.7 virginica\n", "145 5.2 virginica\n", "146 5.0 virginica\n", "147 5.2 virginica\n", "148 5.4 virginica\n", "149 5.1 virginica\n", "\n", "[150 rows x 2 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['petal_length', 'species']]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
55.43.91.70.4setosa
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\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", "5 5.4 3.9 1.7 0.4 setosa" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[2:6]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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petal_lengthspecies
21.3setosa
31.5setosa
41.4setosa
51.7setosa
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" ], "text/plain": [ " petal_length species\n", "2 1.3 setosa\n", "3 1.5 setosa\n", "4 1.4 setosa\n", "5 1.7 setosa" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['petal_length', 'species']][2:6]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
55.43.91.70.4setosa
64.63.41.40.3setosa
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\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", "5 5.4 3.9 1.7 0.4 setosa\n", "6 4.6 3.4 1.4 0.3 setosa" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[2:6]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 setosa\n", "1 setosa\n", "2 setosa\n", "3 setosa\n", "4 setosa\n", "5 setosa\n", "6 setosa\n", "7 setosa\n", "8 setosa\n", "9 setosa\n", "10 setosa\n", "11 setosa\n", "12 setosa\n", "13 setosa\n", "14 setosa\n", "15 setosa\n", "16 setosa\n", "17 setosa\n", "18 setosa\n", "19 setosa\n", "20 setosa\n", "21 setosa\n", "22 setosa\n", "23 setosa\n", "24 setosa\n", "25 setosa\n", "26 setosa\n", "27 setosa\n", "28 setosa\n", "29 setosa\n", " ... \n", "120 virginica\n", "121 virginica\n", "122 virginica\n", "123 virginica\n", "124 virginica\n", "125 virginica\n", "126 virginica\n", "127 virginica\n", "128 virginica\n", "129 virginica\n", "130 virginica\n", "131 virginica\n", "132 virginica\n", "133 virginica\n", "134 virginica\n", "135 virginica\n", "136 virginica\n", "137 virginica\n", "138 virginica\n", "139 virginica\n", "140 virginica\n", "141 virginica\n", "142 virginica\n", "143 virginica\n", "144 virginica\n", "145 virginica\n", "146 virginica\n", "147 virginica\n", "148 virginica\n", "149 virginica\n", "Name: species, Length: 150, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:, 'species']" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthspecies
05.1setosa
14.9setosa
24.7setosa
34.6setosa
45.0setosa
55.4setosa
64.6setosa
75.0setosa
84.4setosa
94.9setosa
105.4setosa
114.8setosa
124.8setosa
134.3setosa
145.8setosa
155.7setosa
165.4setosa
175.1setosa
185.7setosa
195.1setosa
205.4setosa
215.1setosa
224.6setosa
235.1setosa
244.8setosa
255.0setosa
265.0setosa
275.2setosa
285.2setosa
294.7setosa
.........
1206.9virginica
1215.6virginica
1227.7virginica
1236.3virginica
1246.7virginica
1257.2virginica
1266.2virginica
1276.1virginica
1286.4virginica
1297.2virginica
1307.4virginica
1317.9virginica
1326.4virginica
1336.3virginica
1346.1virginica
1357.7virginica
1366.3virginica
1376.4virginica
1386.0virginica
1396.9virginica
1406.7virginica
1416.9virginica
1425.8virginica
1436.8virginica
1446.7virginica
1456.7virginica
1466.3virginica
1476.5virginica
1486.2virginica
1495.9virginica
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150 rows × 2 columns

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" ], "text/plain": [ " sepal_length species\n", "0 5.1 setosa\n", "1 4.9 setosa\n", "2 4.7 setosa\n", "3 4.6 setosa\n", "4 5.0 setosa\n", "5 5.4 setosa\n", "6 4.6 setosa\n", "7 5.0 setosa\n", "8 4.4 setosa\n", "9 4.9 setosa\n", "10 5.4 setosa\n", "11 4.8 setosa\n", "12 4.8 setosa\n", "13 4.3 setosa\n", "14 5.8 setosa\n", "15 5.7 setosa\n", "16 5.4 setosa\n", "17 5.1 setosa\n", "18 5.7 setosa\n", "19 5.1 setosa\n", "20 5.4 setosa\n", "21 5.1 setosa\n", "22 4.6 setosa\n", "23 5.1 setosa\n", "24 4.8 setosa\n", "25 5.0 setosa\n", "26 5.0 setosa\n", "27 5.2 setosa\n", "28 5.2 setosa\n", "29 4.7 setosa\n", ".. ... ...\n", "120 6.9 virginica\n", "121 5.6 virginica\n", "122 7.7 virginica\n", "123 6.3 virginica\n", "124 6.7 virginica\n", "125 7.2 virginica\n", "126 6.2 virginica\n", "127 6.1 virginica\n", "128 6.4 virginica\n", "129 7.2 virginica\n", "130 7.4 virginica\n", "131 7.9 virginica\n", "132 6.4 virginica\n", "133 6.3 virginica\n", "134 6.1 virginica\n", "135 7.7 virginica\n", "136 6.3 virginica\n", "137 6.4 virginica\n", "138 6.0 virginica\n", "139 6.9 virginica\n", "140 6.7 virginica\n", "141 6.9 virginica\n", "142 5.8 virginica\n", "143 6.8 virginica\n", "144 6.7 virginica\n", "145 6.7 virginica\n", "146 6.3 virginica\n", "147 6.5 virginica\n", "148 6.2 virginica\n", "149 5.9 virginica\n", "\n", "[150 rows x 2 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:, ['sepal_length', 'species']]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthspecies
24.7setosa
34.6setosa
45.0setosa
55.4setosa
64.6setosa
\n", "
" ], "text/plain": [ " sepal_length species\n", "2 4.7 setosa\n", "3 4.6 setosa\n", "4 5.0 setosa\n", "5 5.4 setosa\n", "6 4.6 setosa" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[2:6, ['sepal_length', 'species']]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "sepal_length 4.7\n", "sepal_width 3.2\n", "petal_length 1.3\n", "petal_width 0.2\n", "species setosa\n", "Name: 2, dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[2]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2 3.2\n", "3 3.1\n", "Name: sepal_width, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[2:4, 1]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'setosa'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.at[3, 'species']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
14.93.01.40.2setosa
34.63.11.50.2setosa
55.43.91.70.4setosa
75.03.41.50.2setosa
94.93.11.50.1setosa
\n", "
" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "1 4.9 3.0 1.4 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "5 5.4 3.9 1.7 0.4 setosa\n", "7 5.0 3.4 1.5 0.2 setosa\n", "9 4.9 3.1 1.5 0.1 setosa" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[1:10:2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## Boolean selects" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 True\n", "2 True\n", "3 True\n", "4 True\n", "5 True\n", "6 True\n", "7 True\n", "8 True\n", "9 True\n", "10 True\n", "11 True\n", "12 True\n", "13 True\n", "14 True\n", "15 True\n", "16 True\n", "17 True\n", "18 True\n", "19 True\n", "20 True\n", "21 True\n", "22 True\n", "23 True\n", "24 True\n", "25 True\n", "26 True\n", "27 True\n", "28 True\n", "29 True\n", " ... \n", "120 False\n", "121 False\n", "122 False\n", "123 False\n", "124 False\n", "125 False\n", "126 False\n", "127 False\n", "128 False\n", "129 False\n", "130 False\n", "131 False\n", "132 False\n", "133 False\n", "134 False\n", "135 False\n", "136 False\n", "137 False\n", "138 False\n", "139 False\n", "140 False\n", "141 False\n", "142 False\n", "143 False\n", "144 False\n", "145 False\n", "146 False\n", "147 False\n", "148 False\n", "149 False\n", "Name: species, Length: 150, dtype: bool" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:, 'species'] == 'setosa'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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507.03.24.71.4versicolor
516.43.24.51.5versicolor
526.93.14.91.5versicolor
535.52.34.01.3versicolor
546.52.84.61.5versicolor
555.72.84.51.3versicolor
566.33.34.71.6versicolor
574.92.43.31.0versicolor
586.62.94.61.3versicolor
595.22.73.91.4versicolor
605.02.03.51.0versicolor
615.93.04.21.5versicolor
626.02.24.01.0versicolor
636.12.94.71.4versicolor
645.62.93.61.3versicolor
656.73.14.41.4versicolor
665.63.04.51.5versicolor
675.82.74.11.0versicolor
686.22.24.51.5versicolor
695.62.53.91.1versicolor
705.93.24.81.8versicolor
716.12.84.01.3versicolor
726.32.54.91.5versicolor
736.12.84.71.2versicolor
746.42.94.31.3versicolor
756.63.04.41.4versicolor
766.82.84.81.4versicolor
776.73.05.01.7versicolor
786.02.94.51.5versicolor
795.72.63.51.0versicolor
805.52.43.81.1versicolor
815.52.43.71.0versicolor
825.82.73.91.2versicolor
836.02.75.11.6versicolor
845.43.04.51.5versicolor
856.03.44.51.6versicolor
866.73.14.71.5versicolor
876.32.34.41.3versicolor
885.63.04.11.3versicolor
895.52.54.01.3versicolor
905.52.64.41.2versicolor
916.13.04.61.4versicolor
925.82.64.01.2versicolor
935.02.33.31.0versicolor
945.62.74.21.3versicolor
955.73.04.21.2versicolor
965.72.94.21.3versicolor
976.22.94.31.3versicolor
985.12.53.01.1versicolor
995.72.84.11.3versicolor
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "50 7.0 3.2 4.7 1.4 versicolor\n", "51 6.4 3.2 4.5 1.5 versicolor\n", "52 6.9 3.1 4.9 1.5 versicolor\n", "53 5.5 2.3 4.0 1.3 versicolor\n", "54 6.5 2.8 4.6 1.5 versicolor\n", "55 5.7 2.8 4.5 1.3 versicolor\n", "56 6.3 3.3 4.7 1.6 versicolor\n", "57 4.9 2.4 3.3 1.0 versicolor\n", "58 6.6 2.9 4.6 1.3 versicolor\n", "59 5.2 2.7 3.9 1.4 versicolor\n", "60 5.0 2.0 3.5 1.0 versicolor\n", "61 5.9 3.0 4.2 1.5 versicolor\n", "62 6.0 2.2 4.0 1.0 versicolor\n", "63 6.1 2.9 4.7 1.4 versicolor\n", "64 5.6 2.9 3.6 1.3 versicolor\n", "65 6.7 3.1 4.4 1.4 versicolor\n", "66 5.6 3.0 4.5 1.5 versicolor\n", "67 5.8 2.7 4.1 1.0 versicolor\n", "68 6.2 2.2 4.5 1.5 versicolor\n", "69 5.6 2.5 3.9 1.1 versicolor\n", "70 5.9 3.2 4.8 1.8 versicolor\n", "71 6.1 2.8 4.0 1.3 versicolor\n", "72 6.3 2.5 4.9 1.5 versicolor\n", "73 6.1 2.8 4.7 1.2 versicolor\n", "74 6.4 2.9 4.3 1.3 versicolor\n", "75 6.6 3.0 4.4 1.4 versicolor\n", "76 6.8 2.8 4.8 1.4 versicolor\n", "77 6.7 3.0 5.0 1.7 versicolor\n", "78 6.0 2.9 4.5 1.5 versicolor\n", "79 5.7 2.6 3.5 1.0 versicolor\n", "80 5.5 2.4 3.8 1.1 versicolor\n", "81 5.5 2.4 3.7 1.0 versicolor\n", "82 5.8 2.7 3.9 1.2 versicolor\n", "83 6.0 2.7 5.1 1.6 versicolor\n", "84 5.4 3.0 4.5 1.5 versicolor\n", "85 6.0 3.4 4.5 1.6 versicolor\n", "86 6.7 3.1 4.7 1.5 versicolor\n", "87 6.3 2.3 4.4 1.3 versicolor\n", "88 5.6 3.0 4.1 1.3 versicolor\n", "89 5.5 2.5 4.0 1.3 versicolor\n", "90 5.5 2.6 4.4 1.2 versicolor\n", "91 6.1 3.0 4.6 1.4 versicolor\n", "92 5.8 2.6 4.0 1.2 versicolor\n", "93 5.0 2.3 3.3 1.0 versicolor\n", "94 5.6 2.7 4.2 1.3 versicolor\n", "95 5.7 3.0 4.2 1.2 versicolor\n", "96 5.7 2.9 4.2 1.3 versicolor\n", "97 6.2 2.9 4.3 1.3 versicolor\n", "98 5.1 2.5 3.0 1.1 versicolor\n", "99 5.7 2.8 4.1 1.3 versicolor" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df.loc[:, 'species'] == 'versicolor']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "x = df.loc[df.loc[:, 'species'] == 'versicolor']" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sepal_length 6.4\n", "sepal_width 3.2\n", "petal_length 4.5\n", "petal_width 1.5\n", "species versicolor\n", "Name: 51, dtype: object" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.loc[51]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sepal_length 6.4\n", "sepal_width 3.2\n", "petal_length 4.5\n", "petal_width 1.5\n", "species versicolor\n", "Name: 51, dtype: object" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.iloc[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## Summary statictics" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3virginica
1495.93.05.11.8virginica
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max7.900003.8000006.9000002.50000
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "count 50.00000 50.000000 50.000000 50.00000\n", "mean 6.58800 2.974000 5.552000 2.02600\n", "std 0.63588 0.322497 0.551895 0.27465\n", "min 4.90000 2.200000 4.500000 1.40000\n", "25% 6.22500 2.800000 5.100000 1.80000\n", "50% 6.50000 3.000000 5.550000 2.00000\n", "75% 6.90000 3.175000 5.875000 2.30000\n", "max 7.90000 3.800000 6.900000 2.50000" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(df.loc[df.loc[:, 'species'] == 'virginica']).describe()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sepal_length 5.843333\n", "sepal_width 3.054000\n", "petal_length 3.758667\n", "petal_width 1.198667\n", "dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## Plots" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\mclou\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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