pandas/Untitled.ipynb

Untitled.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = pd.read_excel(r'D:\\temp\\sample.xlsx', index_col=0)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df",
      "execution_count": 16,
      "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>value</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-03-31 22:07:48</th>\n      <td>93.25</td>\n    </tr>\n    <tr>\n      <th>2013-03-31 22:07:49</th>\n      <td>93.30</td>\n    </tr>\n    <tr>\n      <th>2013-03-31 22:08:13</th>\n      <td>100.00</td>\n    </tr>\n    <tr>\n      <th>2014-03-31 22:08:14</th>\n      <td>100.00</td>\n    </tr>\n    <tr>\n      <th>2014-03-31 22:08:16</th>\n      <td>100.00</td>\n    </tr>\n    <tr>\n      <th>2014-03-31 22:08:19</th>\n      <td>100.00</td>\n    </tr>\n    <tr>\n      <th>2015-03-31 22:08:21</th>\n      <td>100.00</td>\n    </tr>\n    <tr>\n      <th>2015-03-31 22:08:25</th>\n      <td>100.00</td>\n    </tr>\n    <tr>\n      <th>2015-03-31 22:08:29</th>\n      <td>100.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                      value\ndate                       \n2013-03-31 22:07:48   93.25\n2013-03-31 22:07:49   93.30\n2013-03-31 22:08:13  100.00\n2014-03-31 22:08:14  100.00\n2014-03-31 22:08:16  100.00\n2014-03-31 22:08:19  100.00\n2015-03-31 22:08:21  100.00\n2015-03-31 22:08:25  100.00\n2015-03-31 22:08:29  100.00"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "min_year = df.index.min().year\nmax_year = df.index.max().year\nstr_years = [str(year) for year in range(min_year, max_year + 1)]",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "str_years",
      "execution_count": 31,
      "outputs": [
        {
          "data": {
            "text/plain": "['2013', '2014', '2015']"
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataframe_list_by_year = [df[year] for year in str_years]",
      "execution_count": 40,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataframe_list_by_year",
      "execution_count": 41,
      "outputs": [
        {
          "data": {
            "text/plain": "[                      value\n date                       \n 2013-03-31 22:07:48   93.25\n 2013-03-31 22:07:49   93.30\n 2013-03-31 22:08:13  100.00,                      value\n date                      \n 2014-03-31 22:08:14  100.0\n 2014-03-31 22:08:16  100.0\n 2014-03-31 22:08:19  100.0,                      value\n date                      \n 2015-03-31 22:08:21  100.0\n 2015-03-31 22:08:25  100.0\n 2015-03-31 22:08:29  100.0]"
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "len(dataframe_list)",
      "execution_count": 34,
      "outputs": [
        {
          "data": {
            "text/plain": "3"
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "monthly_index = [date.strftime(\"%Y-%m\") for date in df.resample('M').count().index]",
      "execution_count": 53,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "monthly_index",
      "execution_count": 54,
      "outputs": [
        {
          "data": {
            "text/plain": "['2013-03',\n '2013-04',\n '2013-05',\n '2013-06',\n '2013-07',\n '2013-08',\n '2013-09',\n '2013-10',\n '2013-11',\n '2013-12',\n '2014-01',\n '2014-02',\n '2014-03',\n '2014-04',\n '2014-05',\n '2014-06',\n '2014-07',\n '2014-08',\n '2014-09',\n '2014-10',\n '2014-11',\n '2014-12',\n '2015-01',\n '2015-02',\n '2015-03']"
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataframe_list_by_month = [df[month] for month in monthly_index]",
      "execution_count": 55,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['2015-03']",
      "execution_count": 58,
      "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>value</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2015-03-31 22:08:21</th>\n      <td>100.0</td>\n    </tr>\n    <tr>\n      <th>2015-03-31 22:08:25</th>\n      <td>100.0</td>\n    </tr>\n    <tr>\n      <th>2015-03-31 22:08:29</th>\n      <td>100.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                     value\ndate                      \n2015-03-31 22:08:21  100.0\n2015-03-31 22:08:25  100.0\n2015-03-31 22:08:29  100.0"
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataframe_list_by_month",
      "execution_count": 56,
      "outputs": [
        {
          "data": {
            "text/plain": "[                      value\n date                       \n 2013-03-31 22:07:48   93.25\n 2013-03-31 22:07:49   93.30\n 2013-03-31 22:08:13  100.00, Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [],                      value\n date                      \n 2014-03-31 22:08:14  100.0\n 2014-03-31 22:08:16  100.0\n 2014-03-31 22:08:19  100.0, Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [], Empty DataFrame\n Columns: [value]\n Index: [],                      value\n date                      \n 2015-03-31 22:08:21  100.0\n 2015-03-31 22:08:25  100.0\n 2015-03-31 22:08:29  100.0]"
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    }
  ],
  "metadata": {
    "hide_input": false,
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "pandas/Untitled.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}