learn_pandas_20200902_数据透视.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    \"./datas/movielens-1m/ratings.dat\",\n",
    "    header=None,\n",
    "    names=\"UserID::MovieID::Rating::Timestamp\".split(\"::\"),\n",
    "    sep=\"::\",\n",
    "    engine=\"python\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   UserID  MovieID  Rating  Timestamp\n0       1     1193       5  978300760\n1       1      661       3  978302109\n2       1      914       3  978301968\n3       1     3408       4  978300275\n4       1     2355       5  978824291",
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     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pdate'] = pd.to_datetime(df['Timestamp'], unit = 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   UserID  MovieID  Rating  Timestamp               pdate\n0       1     1193       5  978300760 2000-12-31 22:12:40\n1       1      661       3  978302109 2000-12-31 22:35:09\n2       1      914       3  978301968 2000-12-31 22:32:48\n3       1     3408       4  978300275 2000-12-31 22:04:35\n4       1     2355       5  978824291 2001-01-06 23:38:11",
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     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group = df.groupby([df['pdate'].dt.month, 'Rating'])['UserID'].agg(pv = np.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                pv\npdate Rating      \n1     1       1127\n      2       2608\n      3       6442\n      4       8400\n      5       4495\n2     1        629\n      2       1464\n      3       3297\n      4       4403\n      5       2335\n3     1        466\n      2       1077\n      3       2523\n      4       3032\n      5       1439\n4     1       1048\n      2       2247\n      3       5501\n      4       6748\n      5       3863",
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     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "df_group.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stack = df_group.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "           pv                             \nRating      1      2      3       4      5\npdate                                     \n1        1127   2608   6442    8400   4495\n2         629   1464   3297    4403   2335\n3         466   1077   2523    3032   1439\n4        1048   2247   5501    6748   3863\n5        4557   7631  18481   25769  17840\n6        3196   6500  15211   21838  14365\n7        4891   9566  25421   34957  22169\n8       10873  20597  50509   64198  42497\n9        3107   5873  14702   19927  13182\n10       2121   4785  12175   16095  10324\n11      17701  32202  76069  102448  67041\n12       6458  13007  30866   41156  26760",
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     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "df_stack"
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": "<AxesSubplot:xlabel='pdate'>"
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     "metadata": {},
     "execution_count": 16
    },
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df_stack.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                pv\npdate Rating      \n1     1       1127\n      2       2608\n      3       6442\n      4       8400\n      5       4495",
      "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></th>\n      <th>pv</th>\n    </tr>\n    <tr>\n      <th>pdate</th>\n      <th>Rating</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">1</th>\n      <th>1</th>\n      <td>1127</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2608</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>6442</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8400</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4495</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "df_group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reset = df_group.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   pdate  Rating    pv\n0      1       1  1127\n1      1       2  2608\n2      1       3  6442\n3      1       4  8400\n4      1       5  4495",
      "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>pdate</th>\n      <th>Rating</th>\n      <th>pv</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1127</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2</td>\n      <td>2608</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>6442</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>4</td>\n      <td>8400</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>5</td>\n      <td>4495</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "df_reset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot = df_reset.pivot('pdate', 'Rating', 'pv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Rating     1     2      3      4      5\npdate                                  \n1       1127  2608   6442   8400   4495\n2        629  1464   3297   4403   2335\n3        466  1077   2523   3032   1439\n4       1048  2247   5501   6748   3863\n5       4557  7631  18481  25769  17840",
      "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>Rating</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n    </tr>\n    <tr>\n      <th>pdate</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1127</td>\n      <td>2608</td>\n      <td>6442</td>\n      <td>8400</td>\n      <td>4495</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>629</td>\n      <td>1464</td>\n      <td>3297</td>\n      <td>4403</td>\n      <td>2335</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>466</td>\n      <td>1077</td>\n      <td>2523</td>\n      <td>3032</td>\n      <td>1439</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1048</td>\n      <td>2247</td>\n      <td>5501</td>\n      <td>6748</td>\n      <td>3863</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4557</td>\n      <td>7631</td>\n      <td>18481</td>\n      <td>25769</td>\n      <td>17840</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "df_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:xlabel='pdate'>"
     },
     "metadata": {},
     "execution_count": 22
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df_pivot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***pivot相当于对df使用set_index创建分层索引，然后调用unstack***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}