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Hummel #12

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Hummel #12

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819a687
test
Oct 12, 2018
410c1c0
jupyter notebook 1
Oct 25, 2018
bc4e518
iris
Oct 25, 2018
f4ae221
2c example
Oct 25, 2018
1fd477b
jupyter notebook final
Oct 25, 2018
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217 changes: 217 additions & 0 deletions Exercise7.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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\n",
"11 4.8 3.4 1.6 0.2 setosa\n",
"13 4.3 3.0 1.1 0.1 setosa\n",
"15 5.7 4.4 1.5 0.4 setosa\n",
"17 5.1 3.5 1.4 0.3 setosa\n",
"19 5.1 3.8 1.5 0.3 setosa\n",
"21 5.1 3.7 1.5 0.4 setosa\n",
"23 5.1 3.3 1.7 0.5 setosa\n",
"25 5.0 3.0 1.6 0.2 setosa\n",
"27 5.2 3.5 1.5 0.2 setosa\n",
"29 4.7 3.2 1.6 0.2 setosa\n",
"31 5.4 3.4 1.5 0.4 setosa\n",
"33 5.5 4.2 1.4 0.2 setosa\n",
"35 5.0 3.2 1.2 0.2 setosa\n",
"37 4.9 3.6 1.4 0.1 setosa\n",
"39 5.1 3.4 1.5 0.2 setosa\n",
"41 4.5 2.3 1.3 0.3 setosa\n",
"43 5.0 3.5 1.6 0.6 setosa\n",
"45 4.8 3.0 1.4 0.3 setosa\n",
"47 4.6 3.2 1.4 0.2 setosa\n",
"49 5.0 3.3 1.4 0.2 setosa\n",
"51 6.4 3.2 4.5 1.5 versicolor\n",
"53 5.5 2.3 4.0 1.3 versicolor\n",
"55 5.7 2.8 4.5 1.3 versicolor\n",
"57 4.9 2.4 3.3 1.0 versicolor\n",
"59 5.2 2.7 3.9 1.4 versicolor\n",
".. ... ... ... ... ...\n",
"91 6.1 3.0 4.6 1.4 versicolor\n",
"93 5.0 2.3 3.3 1.0 versicolor\n",
"95 5.7 3.0 4.2 1.2 versicolor\n",
"97 6.2 2.9 4.3 1.3 versicolor\n",
"99 5.7 2.8 4.1 1.3 versicolor\n",
"101 5.8 2.7 5.1 1.9 virginica\n",
"103 6.3 2.9 5.6 1.8 virginica\n",
"105 7.6 3.0 6.6 2.1 virginica\n",
"107 7.3 2.9 6.3 1.8 virginica\n",
"109 7.2 3.6 6.1 2.5 virginica\n",
"111 6.4 2.7 5.3 1.9 virginica\n",
"113 5.7 2.5 5.0 2.0 virginica\n",
"115 6.4 3.2 5.3 2.3 virginica\n",
"117 7.7 3.8 6.7 2.2 virginica\n",
"119 6.0 2.2 5.0 1.5 virginica\n",
"121 5.6 2.8 4.9 2.0 virginica\n",
"123 6.3 2.7 4.9 1.8 virginica\n",
"125 7.2 3.2 6.0 1.8 virginica\n",
"127 6.1 3.0 4.9 1.8 virginica\n",
"129 7.2 3.0 5.8 1.6 virginica\n",
"131 7.9 3.8 6.4 2.0 virginica\n",
"133 6.3 2.8 5.1 1.5 virginica\n",
"135 7.7 3.0 6.1 2.3 virginica\n",
"137 6.4 3.1 5.5 1.8 virginica\n",
"139 6.9 3.1 5.4 2.1 virginica\n",
"141 6.9 3.1 5.1 2.3 virginica\n",
"143 6.8 3.2 5.9 2.3 virginica\n",
"145 6.7 3.0 5.2 2.3 virginica\n",
"147 6.5 3.0 5.2 2.0 virginica\n",
"149 5.9 3.0 5.1 1.8 virginica\n",
"\n",
"[75 rows x 5 columns]\n"
]
}
],
"source": [
"#1. Print odd rows in a pandas dataset\n",
"import pandas as pd\n",
"df = pd.read_csv('iris.csv')\n",
"def odd_rows(dataframe):\n",
" return (dataframe[1::2])\n",
"print odd_rows(df)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"versicolor 50\n",
"Name: Species, dtype: int64\n"
]
}
],
"source": [
"#2a. return the number of observations of a given species included in the dataset\n",
"import pandas as pd\n",
"data = pd.read_csv('iris.csv')\n",
"\n",
"def species_count(species):\n",
" speciesrows=data[data.Species.str.contains(species)]\n",
" return speciesrows['Species'].value_counts()\n",
"\n",
"print species_count('versicolor')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
"4 5.0 3.6 1.4 0.2 setosa\n",
"5 5.4 3.9 1.7 0.4 setosa\n",
"10 5.4 3.7 1.5 0.2 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",
"18 5.7 3.8 1.7 0.3 setosa\n",
"19 5.1 3.8 1.5 0.3 setosa\n",
"21 5.1 3.7 1.5 0.4 setosa\n",
"22 4.6 3.6 1.0 0.2 setosa\n",
"32 5.2 4.1 1.5 0.1 setosa\n",
"33 5.5 4.2 1.4 0.2 setosa\n",
"37 4.9 3.6 1.4 0.1 setosa\n",
"44 5.1 3.8 1.9 0.4 setosa\n",
"46 5.1 3.8 1.6 0.2 setosa\n",
"48 5.3 3.7 1.5 0.2 setosa\n",
"109 7.2 3.6 6.1 2.5 virginica\n",
"117 7.7 3.8 6.7 2.2 virginica\n",
"131 7.9 3.8 6.4 2.0 virginica\n"
]
}
],
"source": [
"#2b. return a dataframe for flowers with Sepal.Width greater than a value specified by the function use\n",
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"def Sepal_Width(width):\n",
" return (df[df['Sepal.Width'] > width])\n",
"print Sepal_Width(3.5)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "EOL while scanning string literal (<ipython-input-17-d6064860f4fa>, line 3)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-17-d6064860f4fa>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m df = pandas.read_csv('iris.csv’)\u001b[0m\n\u001b[0m \n^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
]
}
],
"source": [
"#2c. Write the data for a given species to a comma-delimited file with the given species name as the file name; Hint: look at using + or % to concatenate your species name and \".csv\"\n",
"import pandas\n",
"df = pandas.read_csv('iris.csv’)\n",
"\n",
"def new_species_file(species):\n",
" name=df[df.Species.str.contains(species)]\n",
" return (name.to_csv(path_or_buf='species2.csv', sep=',’))\n",
"\n",
"print new_species_file('versicolor')\n",
" \n",
"#Problem with jupyter notebook, but should work in spyder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
151 changes: 151 additions & 0 deletions iris.csv
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"Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
5.1,3.5,1.4,0.2,"setosa"
4.9,3,1.4,0.2,"setosa"
4.7,3.2,1.3,0.2,"setosa"
4.6,3.1,1.5,0.2,"setosa"
5,3.6,1.4,0.2,"setosa"
5.4,3.9,1.7,0.4,"setosa"
4.6,3.4,1.4,0.3,"setosa"
5,3.4,1.5,0.2,"setosa"
4.4,2.9,1.4,0.2,"setosa"
4.9,3.1,1.5,0.1,"setosa"
5.4,3.7,1.5,0.2,"setosa"
4.8,3.4,1.6,0.2,"setosa"
4.8,3,1.4,0.1,"setosa"
4.3,3,1.1,0.1,"setosa"
5.8,4,1.2,0.2,"setosa"
5.7,4.4,1.5,0.4,"setosa"
5.4,3.9,1.3,0.4,"setosa"
5.1,3.5,1.4,0.3,"setosa"
5.7,3.8,1.7,0.3,"setosa"
5.1,3.8,1.5,0.3,"setosa"
5.4,3.4,1.7,0.2,"setosa"
5.1,3.7,1.5,0.4,"setosa"
4.6,3.6,1,0.2,"setosa"
5.1,3.3,1.7,0.5,"setosa"
4.8,3.4,1.9,0.2,"setosa"
5,3,1.6,0.2,"setosa"
5,3.4,1.6,0.4,"setosa"
5.2,3.5,1.5,0.2,"setosa"
5.2,3.4,1.4,0.2,"setosa"
4.7,3.2,1.6,0.2,"setosa"
4.8,3.1,1.6,0.2,"setosa"
5.4,3.4,1.5,0.4,"setosa"
5.2,4.1,1.5,0.1,"setosa"
5.5,4.2,1.4,0.2,"setosa"
4.9,3.1,1.5,0.2,"setosa"
5,3.2,1.2,0.2,"setosa"
5.5,3.5,1.3,0.2,"setosa"
4.9,3.6,1.4,0.1,"setosa"
4.4,3,1.3,0.2,"setosa"
5.1,3.4,1.5,0.2,"setosa"
5,3.5,1.3,0.3,"setosa"
4.5,2.3,1.3,0.3,"setosa"
4.4,3.2,1.3,0.2,"setosa"
5,3.5,1.6,0.6,"setosa"
5.1,3.8,1.9,0.4,"setosa"
4.8,3,1.4,0.3,"setosa"
5.1,3.8,1.6,0.2,"setosa"
4.6,3.2,1.4,0.2,"setosa"
5.3,3.7,1.5,0.2,"setosa"
5,3.3,1.4,0.2,"setosa"
7,3.2,4.7,1.4,"versicolor"
6.4,3.2,4.5,1.5,"versicolor"
6.9,3.1,4.9,1.5,"versicolor"
5.5,2.3,4,1.3,"versicolor"
6.5,2.8,4.6,1.5,"versicolor"
5.7,2.8,4.5,1.3,"versicolor"
6.3,3.3,4.7,1.6,"versicolor"
4.9,2.4,3.3,1,"versicolor"
6.6,2.9,4.6,1.3,"versicolor"
5.2,2.7,3.9,1.4,"versicolor"
5,2,3.5,1,"versicolor"
5.9,3,4.2,1.5,"versicolor"
6,2.2,4,1,"versicolor"
6.1,2.9,4.7,1.4,"versicolor"
5.6,2.9,3.6,1.3,"versicolor"
6.7,3.1,4.4,1.4,"versicolor"
5.6,3,4.5,1.5,"versicolor"
5.8,2.7,4.1,1,"versicolor"
6.2,2.2,4.5,1.5,"versicolor"
5.6,2.5,3.9,1.1,"versicolor"
5.9,3.2,4.8,1.8,"versicolor"
6.1,2.8,4,1.3,"versicolor"
6.3,2.5,4.9,1.5,"versicolor"
6.1,2.8,4.7,1.2,"versicolor"
6.4,2.9,4.3,1.3,"versicolor"
6.6,3,4.4,1.4,"versicolor"
6.8,2.8,4.8,1.4,"versicolor"
6.7,3,5,1.7,"versicolor"
6,2.9,4.5,1.5,"versicolor"
5.7,2.6,3.5,1,"versicolor"
5.5,2.4,3.8,1.1,"versicolor"
5.5,2.4,3.7,1,"versicolor"
5.8,2.7,3.9,1.2,"versicolor"
6,2.7,5.1,1.6,"versicolor"
5.4,3,4.5,1.5,"versicolor"
6,3.4,4.5,1.6,"versicolor"
6.7,3.1,4.7,1.5,"versicolor"
6.3,2.3,4.4,1.3,"versicolor"
5.6,3,4.1,1.3,"versicolor"
5.5,2.5,4,1.3,"versicolor"
5.5,2.6,4.4,1.2,"versicolor"
6.1,3,4.6,1.4,"versicolor"
5.8,2.6,4,1.2,"versicolor"
5,2.3,3.3,1,"versicolor"
5.6,2.7,4.2,1.3,"versicolor"
5.7,3,4.2,1.2,"versicolor"
5.7,2.9,4.2,1.3,"versicolor"
6.2,2.9,4.3,1.3,"versicolor"
5.1,2.5,3,1.1,"versicolor"
5.7,2.8,4.1,1.3,"versicolor"
6.3,3.3,6,2.5,"virginica"
5.8,2.7,5.1,1.9,"virginica"
7.1,3,5.9,2.1,"virginica"
6.3,2.9,5.6,1.8,"virginica"
6.5,3,5.8,2.2,"virginica"
7.6,3,6.6,2.1,"virginica"
4.9,2.5,4.5,1.7,"virginica"
7.3,2.9,6.3,1.8,"virginica"
6.7,2.5,5.8,1.8,"virginica"
7.2,3.6,6.1,2.5,"virginica"
6.5,3.2,5.1,2,"virginica"
6.4,2.7,5.3,1.9,"virginica"
6.8,3,5.5,2.1,"virginica"
5.7,2.5,5,2,"virginica"
5.8,2.8,5.1,2.4,"virginica"
6.4,3.2,5.3,2.3,"virginica"
6.5,3,5.5,1.8,"virginica"
7.7,3.8,6.7,2.2,"virginica"
7.7,2.6,6.9,2.3,"virginica"
6,2.2,5,1.5,"virginica"
6.9,3.2,5.7,2.3,"virginica"
5.6,2.8,4.9,2,"virginica"
7.7,2.8,6.7,2,"virginica"
6.3,2.7,4.9,1.8,"virginica"
6.7,3.3,5.7,2.1,"virginica"
7.2,3.2,6,1.8,"virginica"
6.2,2.8,4.8,1.8,"virginica"
6.1,3,4.9,1.8,"virginica"
6.4,2.8,5.6,2.1,"virginica"
7.2,3,5.8,1.6,"virginica"
7.4,2.8,6.1,1.9,"virginica"
7.9,3.8,6.4,2,"virginica"
6.4,2.8,5.6,2.2,"virginica"
6.3,2.8,5.1,1.5,"virginica"
6.1,2.6,5.6,1.4,"virginica"
7.7,3,6.1,2.3,"virginica"
6.3,3.4,5.6,2.4,"virginica"
6.4,3.1,5.5,1.8,"virginica"
6,3,4.8,1.8,"virginica"
6.9,3.1,5.4,2.1,"virginica"
6.7,3.1,5.6,2.4,"virginica"
6.9,3.1,5.1,2.3,"virginica"
5.8,2.7,5.1,1.9,"virginica"
6.8,3.2,5.9,2.3,"virginica"
6.7,3.3,5.7,2.5,"virginica"
6.7,3,5.2,2.3,"virginica"
6.3,2.5,5,1.9,"virginica"
6.5,3,5.2,2,"virginica"
6.2,3.4,5.4,2.3,"virginica"
5.9,3,5.1,1.8,"virginica"
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