Pandas_function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc45f317",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "658c94be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'scores':[23,100,67],'age':[12,34,1]},index=['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1717e964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scores    63.333333\n",
       "age       15.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db7b7665",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scores</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
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       "      <th>b</th>\n",
       "      <td>100</td>\n",
       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   scores  age\n",
       "a      23   12\n",
       "b     100   34\n",
       "c      67    1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f11be39f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['a','scores']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37b77797",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['a','scores']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b39a3a2",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "a     NaN   12\n",
       "b   100.0   34\n",
       "c    67.0    1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5dc69a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scores    83.500000\n",
       "age       15.666667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()#自动跳过缺失的值，按照列做平均数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1679b5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    12.0\n",
       "b    67.0\n",
       "c    34.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=1)#按行求平均数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b5d9916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scores    167.0\n",
       "age        47.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()#按列求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f8272ac",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "a     NaN   12\n",
       "b   100.0   34\n",
       "c    67.0    1"
      ]
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "634cb7cc",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "c    67.0    1\n",
       "a     NaN   12\n",
       "b   100.0   34"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='age')#按照age进行排序,默认为升序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88e5fb2f",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "b   100.0   34\n",
       "a     NaN   12\n",
       "c    67.0    1"
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "df.sort_values(by='age',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5dcd9738",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "c    67.0    1\n",
       "b   100.0   34\n",
       "a     NaN   12"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='scores')#nan默认放在最后面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6246bab7",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "b   100.0   34\n",
       "c    67.0    1\n",
       "a     NaN   12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='scores',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cfa1a6c5",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "a     NaN   12\n",
       "b   100.0   34\n",
       "c    67.0    1"
      ]
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index()#按照指标排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2bf8fe9a",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   scores  age\n",
       "a     NaN   12\n",
       "b   100.0   34\n",
       "c    67.0    1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
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    }
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e484971f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'Mary':[1,2],'H':[3,9]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea256fc3",
   "metadata": {},
   "outputs": [
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       "   Mary  H\n",
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       "1     2  9"
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     "execution_count": 3,
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    "df"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f988db71",
   "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>Mary</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mary  H\n",
       "1     2  9\n",
       "0     1  3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='Mary',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7dd4ca2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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