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Restore working notebook
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‎source-code/pandas/missing_values.ipynb

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{
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"metadata": {},
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"outputs": [],
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"source": [
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data1.info()"
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>int_data</th>\n",
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" <th>0</th>\n",
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" <td>str1_str1</td>\n",
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" <td>3.5</td>\n",
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" <td>A</td>\n",
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" <td>str1_str2</td>\n",
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" <td>13.0</td>\n",
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" <td>A</td>\n",
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" <td>str2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>23.0</td>\n",
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" <td>7.1</td>\n",
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" <td>B</td>\n",
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" <td>str2_str2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>NaN</td>\n",
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" <td>5.5</td>\n",
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" <td>A</td>\n",
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" <td>str3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>29.0</td>\n",
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" <td>NaN</td>\n",
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" <td>B</td>\n",
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" <td>str3_str1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>31.0</td>\n",
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" <td>3.3</td>\n",
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" <td>NaN</td>\n",
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" <td>str2_str3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>37.0</td>\n",
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" <td>7.7</td>\n",
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" <td>B</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" int_data float_data category_data string_data\n",
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"0 3.0 3.7 A str1\n",
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"1 5.0 5.3 A str1_str1\n",
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"2 7.0 7.5 B str2_str1\n",
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"3 17.0 3.5 A str1_str2\n",
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"4 13.0 5.7 A str2\n",
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"5 23.0 7.1 B str2_str2\n",
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"6 NaN 5.5 A str3\n",
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"7 29.0 NaN B str3_str1\n",
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"8 31.0 3.3 NaN str2_str3\n",
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"9 37.0 7.7 B NaN"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"cell_type": "code",
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"source": [
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"cell_type": "code",
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{
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"9 37 7.7 B <NA>"
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]
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},
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"execution_count": 6,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"165"
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]
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"execution_count": 9,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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" <tr>\n",
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" <th>top</th>\n",
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" <td>A</td>\n",
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" <td>str3</td>\n",
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" <td>str1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>freq</th>\n",
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" category_data string_data\n",
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"count 9 9\n",
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"unique 2 9\n",
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"top A str3\n,
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"top A str1\n",
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"freq 5 1"
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]
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},
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"toc-autonumbering": true
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},
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"nbformat": 4,
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"nbformat_minor": 4

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