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Pandas / 02 - Learning Pandas / 04 - Dealing with Missing Data in Pandas
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04 - Dealing with Missing Data in Pandas.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>Fruits</th>\n",
" <th>Extra Data</th>\n",
" <th>G</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-07-01</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>apple</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-02</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>orange</td>\n",
" <td>7</td>\n",
" <td>107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-03</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>banana</td>\n",
" <td>10</td>\n",
" <td>207</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-04</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" <td>strawberry</td>\n",
" <td>13</td>\n",
" <td>307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-05</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" <td>blueberry</td>\n",
" <td>16</td>\n",
" <td>407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-07-06</th>\n",
" <td>20</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>23</td>\n",
" <td>pineapple</td>\n",
" <td>19</td>\n",
" <td>507</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D Fruits Extra Data G\n",
"2016-07-01 0 1 2 3 apple 4 7\n",
"2016-07-02 4 5 6 7 orange 7 107\n",
"2016-07-03 8 9 10 11 banana 10 207\n",
"2016-07-04 12 13 14 15 strawberry 13 307\n",
"2016-07-05 16 17 18 19 blueberry 16 407\n",
"2016-07-06 20 21 22 23 pineapple 19 507"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"\n",
"starting_date = '20160701'\n",
"sample_numpy_data = np.array(np.arange(24)).reshape((6,4))\n",
"dates_index = pd.date_range(starting_date, periods=6)\n",
"sample_df = pd.DataFrame(sample_numpy_data, index=dates_index, columns=list('ABCD'))\n",
"\n",
"sample_df_2 = sample_df.copy()\n",
"sample_df_2['Fruits'] = ['apple', 'orange','banana','strawberry','blueberry','pineapple']\n",
"\n",
"sample_series = pd.Series([1,2,3,4,5,6], index=pd.date_range(starting_date, periods=6))\n",
"sample_df_2['Extra Data'] = sample_series *3 +1\n",
"\n",
"second_numpy_array = np.array(np.arange(len(sample_df_2))) *100 + 7\n",
"sample_df_2['G'] = second_numpy_array\n",
"\n",
"sample_df_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing Data\n",
"pandas uses Numpy library np.nan to represent missing data. By default, it is not included in computations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using reindex() method"
]
},
{
"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",
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" vertical-align: top;\n",
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"\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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Firefox</th>\n",
" <td>200</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>404</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Konqueror</th>\n",
" <td>301</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Firefox 200 0.04\n",
"Chrome 200 0.02\n",
"Safari 404 0.07\n",
"IE10 404 0.08\n",
"Konqueror 301 1.00"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']\n",
"\n",
"browser_df = pd.DataFrame({\n",
" 'http_status': [200,200,404,404,301],\n",
" 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},\n",
" index=browser_index)\n",
"browser_df"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### reindex() creates a copy (not a view)"
]
},
{
"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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>404.0</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceweasel</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Comodo Dragon</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404.0</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200.0</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Safari 404.0 0.07\n",
"Iceweasel NaN NaN\n",
"Comodo Dragon NaN NaN\n",
"IE10 404.0 0.08\n",
"Chrome 200.0 0.02"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10', 'Chrome']\n",
"browser_df_2 = browser_df.reindex(new_index) # assigns NaN to cell for which it does not find the matching index.\n",
"browser_df_2"
]
},
{
"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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Firefox</th>\n",
" <td>200</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>404</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Konqueror</th>\n",
" <td>301</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Firefox 200 0.04\n",
"Chrome 200 0.02\n",
"Safari 404 0.07\n",
"IE10 404 0.08\n",
"Konqueror 301 1.00"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_df # comparing above code we can see that the original browser_df dataframe object is still intact with its original indexes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### drop rows that have missing data\n",
"documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>404.0</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404.0</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200.0</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Safari 404.0 0.07\n",
"IE10 404.0 0.08\n",
"Chrome 200.0 0.02"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_df_3 = browser_df_2.dropna(how='any') # ‘any’ : If any NA values are present, drop that row or column.\n",
"browser_df_3"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Firefox</th>\n",
" <td>200.0</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200.0</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>NaN</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404.0</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Konqueror</th>\n",
" <td>301.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Firefox 200.0 0.04\n",
"Chrome 200.0 0.02\n",
"Safari NaN 0.07\n",
"IE10 404.0 0.08\n",
"Konqueror 301.0 1.00"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_df_test = pd.DataFrame({\n",
" 'http_status': [200,200,np.nan,404,301],\n",
" 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},\n",
" index=browser_index)\n",
"browser_df_test"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Firefox</th>\n",
" <td>200.0</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200.0</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>NaN</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404.0</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Konqueror</th>\n",
" <td>301.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Firefox 200.0 0.04\n",
"Chrome 200.0 0.02\n",
"Safari NaN 0.07\n",
"IE10 404.0 0.08\n",
"Konqueror 301.0 1.00"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_df_test.dropna(how='all') # does not drop the row as all the values of the row should be NA"
]
},
{
"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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Firefox</th>\n",
" <td>200.0</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200.0</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404.0</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Konqueror</th>\n",
" <td>301.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Firefox 200.0 0.04\n",
"Chrome 200.0 0.02\n",
"IE10 404.0 0.08\n",
"Konqueror 301.0 1.00"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_df_test.dropna(how='any')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### fill-in missing data\n",
"documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>404.00000</td>\n",
" <td>0.07000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceweasel</th>\n",
" <td>-0.05555</td>\n",
" <td>-0.05555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Comodo Dragon</th>\n",
" <td>-0.05555</td>\n",
" <td>-0.05555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>404.00000</td>\n",
" <td>0.08000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>200.00000</td>\n",
" <td>0.02000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Safari 404.00000 0.07000\n",
"Iceweasel -0.05555 -0.05555\n",
"Comodo Dragon -0.05555 -0.05555\n",
"IE10 404.00000 0.08000\n",
"Chrome 200.00000 0.02000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_df_2.fillna(value=-0.05555)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### get boolean mask where values are NaN"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceweasel</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Comodo Dragon</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Safari False False\n",
"Iceweasel True True\n",
"Comodo Dragon True True\n",
"IE10 False False\n",
"Chrome False False"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(browser_df_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### NaN propagates during arithmetic operations"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>http_status</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Safari</th>\n",
" <td>6868.0</td>\n",
" <td>1.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceweasel</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Comodo Dragon</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IE10</th>\n",
" <td>6868.0</td>\n",
" <td>1.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chrome</th>\n",
" <td>3400.0</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" http_status response_time\n",
"Safari 6868.0 1.19\n",
"Iceweasel NaN NaN\n",
"Comodo Dragon NaN NaN\n",
"IE10 6868.0 1.36\n",
"Chrome 3400.0 0.34"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"browser_df_2 * 17"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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