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Tatarian #6

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982a0f6
sentosa
Oct 11, 2018
3a730a2
6
Oct 11, 2018
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327 changes: 327 additions & 0 deletions Exercise 6.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": 8,
"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>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",
" </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"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"line = 5\n",
"df.head(line)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Species\n",
"148 virginica\n",
"149 virginica\n"
]
}
],
"source": [
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"print(df.iloc[148:151, 4:6])\n",
"\n",
"#last 2 rows, last 2 columns"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"setosa 50\n",
"versicolor 50\n",
"virginica 50\n",
"Name: Species, dtype: int64\n"
]
}
],
"source": [
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"counts = df['Species'].value_counts()\n",
"print counts"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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": [
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"length = df[df['Sepal.Width'] > 3.5]\n",
"print(length)\n",
"\n",
"#print sepal width >3.5"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"sentosa = df.iloc[1:50, ]\n",
"sentosa.to_csv(path_or_buf='SentosaOut.csv', sep=',')\n",
"\n",
"#write data for species sentosa to a comma-delimited filed"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6.9, 4.5, 5.552)\n"
]
}
],
"source": [
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"virginica = df.iloc[100:151, ]\n",
"maximum = virginica['Petal.Length'].max()\n",
"minimum = virginica['Petal.Length'].min()\n",
"mean = virginica['Petal.Length'].mean()\n",
"print(maximum, minimum, mean)\n",
"\n",
"\n",
"#calculate the mean, minimum, and maximum of petal.length for observations fom virginica"
]
},
{
"cell_type": "code",
"execution_count": 62,
"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",
"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",
"30 4.8 3.1 1.6 0.2 setosa\n",
"31 5.4 3.4 1.5 0.4 setosa\n",
"32 5.2 4.1 1.5 0.1 setosa\n",
"33 5.5 4.2 1.4 0.2 setosa\n",
"34 4.9 3.1 1.5 0.2 setosa\n",
"35 5.0 3.2 1.2 0.2 setosa\n",
"36 5.5 3.5 1.3 0.2 setosa\n",
"37 4.9 3.6 1.4 0.1 setosa\n",
"38 4.4 3.0 1.3 0.2 setosa\n",
"39 5.1 3.4 1.5 0.2 setosa\n",
"40 5.0 3.5 1.3 0.3 setosa\n",
"41 4.5 2.3 1.3 0.3 setosa\n",
"42 4.4 3.2 1.3 0.2 setosa\n",
"43 5.0 3.5 1.6 0.6 setosa\n",
"44 5.1 3.8 1.9 0.4 setosa\n",
"45 4.8 3.0 1.4 0.3 setosa\n",
"46 5.1 3.8 1.6 0.2 setosa\n",
"47 4.6 3.2 1.4 0.2 setosa\n",
"48 5.3 3.7 1.5 0.2 setosa\n",
"49 5.0 3.3 1.4 0.2 setosa\n"
]
}
],
"source": [
"import pandas\n",
"df = pandas.read_csv('iris.csv')\n",
"sentosa = df.iloc[1:50, ]\n",
"print(sentosa)"
]
},
{
"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
}
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