3.3 pandas汇总统计方法与应用函数.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "996d81aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a69f89f",
   "metadata": {},
   "source": [
    "# 统计方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "942f1cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.random.randn(9,6), columns=list('abcdef'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9fd1692",
   "metadata": {},
   "outputs": [
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       "      <td>-0.838515</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.638295</td>\n",
       "      <td>-0.210192</td>\n",
       "      <td>0.556669</td>\n",
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       "      <td>-1.736242</td>\n",
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       "      <td>-0.135979</td>\n",
       "      <td>-0.486893</td>\n",
       "      <td>0.890941</td>\n",
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       "      <th>8</th>\n",
       "      <td>0.964277</td>\n",
       "      <td>1.297130</td>\n",
       "      <td>-0.521509</td>\n",
       "      <td>-0.057722</td>\n",
       "      <td>-0.240441</td>\n",
       "      <td>-0.036027</td>\n",
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       "          a         b         c         d         e         f\n",
       "0 -1.275374 -1.716434  0.378963 -0.570430 -0.838515 -0.580085\n",
       "1 -0.379262 -0.194995  1.712992  0.647108  0.716119  1.556584\n",
       "2 -0.560927 -1.532025 -0.477870  0.543167  1.043725 -0.748711\n",
       "3  1.628004 -0.179523  1.089616  1.102608  0.333088 -0.942939\n",
       "4 -1.625205 -0.379636  1.757878  0.963458  0.576844  1.185927\n",
       "5 -1.506126 -0.228909  0.136160 -0.638295 -0.210192  0.556669\n",
       "6 -0.612531 -1.607560  0.165254 -1.736242 -1.267426  0.397543\n",
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   "execution_count": 4,
   "id": "ee0be27f",
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   "outputs": [
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       "          a         b         c         d         e         f\n",
       "0 -1.275374 -1.716434  0.378963 -0.570430 -0.838515 -0.580085\n",
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   "source": [
    "data.head(3)"
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   "execution_count": 5,
   "id": "a8110c26",
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   a       9 non-null      float64\n",
      " 1   b       9 non-null      float64\n",
      " 2   c       9 non-null      float64\n",
      " 3   d       9 non-null      float64\n",
      " 4   e       9 non-null      float64\n",
      " 5   f       9 non-null      float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 560.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ad1a6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.iloc[2,2] = np.nan"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "7b898418",
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   a       9 non-null      float64\n",
      " 1   b       9 non-null      float64\n",
      " 2   c       8 non-null      float64\n",
      " 3   d       9 non-null      float64\n",
      " 4   e       9 non-null      float64\n",
      " 5   f       9 non-null      float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 560.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f00295ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
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       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>-0.379832</td>\n",
       "      <td>-0.521974</td>\n",
       "      <td>0.769114</td>\n",
       "      <td>0.013075</td>\n",
       "      <td>-0.041521</td>\n",
       "      <td>0.253322</td>\n",
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       "      <th>std</th>\n",
       "      <td>1.098543</td>\n",
       "      <td>0.961703</td>\n",
       "      <td>0.844814</td>\n",
       "      <td>0.910042</td>\n",
       "      <td>0.764763</td>\n",
       "      <td>0.887484</td>\n",
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       "      <td>-0.521509</td>\n",
       "      <td>-1.736242</td>\n",
       "      <td>-1.267426</td>\n",
       "      <td>-0.942939</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.275374</td>\n",
       "      <td>-1.532025</td>\n",
       "      <td>0.157980</td>\n",
       "      <td>-0.570430</td>\n",
       "      <td>-0.486893</td>\n",
       "      <td>-0.580085</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.560927</td>\n",
       "      <td>-0.228909</td>\n",
       "      <td>0.734290</td>\n",
       "      <td>-0.057722</td>\n",
       "      <td>-0.210192</td>\n",
       "      <td>0.397543</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.051339</td>\n",
       "      <td>-0.179523</td>\n",
       "      <td>1.503418</td>\n",
       "      <td>0.647108</td>\n",
       "      <td>0.576844</td>\n",
       "      <td>0.890941</td>\n",
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       "      <th>max</th>\n",
       "      <td>1.628004</td>\n",
       "      <td>1.297130</td>\n",
       "      <td>1.757878</td>\n",
       "      <td>1.102608</td>\n",
       "      <td>1.043725</td>\n",
       "      <td>1.556584</td>\n",
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      "text/plain": [
       "              a         b         c         d         e         f\n",
       "count  9.000000  9.000000  8.000000  9.000000  9.000000  9.000000\n",
       "mean  -0.379832 -0.521974  0.769114  0.013075 -0.041521  0.253322\n",
       "std    1.098543  0.961703  0.844814  0.910042  0.764763  0.887484\n",
       "min   -1.625205 -1.716434 -0.521509 -1.736242 -1.267426 -0.942939\n",
       "25%   -1.275374 -1.532025  0.157980 -0.570430 -0.486893 -0.580085\n",
       "50%   -0.560927 -0.228909  0.734290 -0.057722 -0.210192  0.397543\n",
       "75%   -0.051339 -0.179523  1.503418  0.647108  0.576844  0.890941\n",
       "max    1.628004  1.297130  1.757878  1.102608  1.043725  1.556584"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "362ab6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -3.418484\n",
       "b   -4.697769\n",
       "c    6.152914\n",
       "d    0.117672\n",
       "e   -0.373692\n",
       "f    2.279902\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sum(0)  #第一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85516197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b    8\n",
       "c    4\n",
       "d    3\n",
       "e    2\n",
       "f    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.idxmax(0)  # 返回最大值索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4399ec74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.a.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19f12fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6280042571252895"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.a[data.a.idxmax()]  # 返回最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68c6df57",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.random.randint(1,10,size=(5,7)))  # 范围1到10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "488f611f",
   "metadata": {},
   "outputs": [
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       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2  3  4  5  6\n",
       "0  4  1  7  8  7  3  6\n",
       "1  1  5  4  5  6  6  9\n",
       "2  4  3  7  5  3  8  3\n",
       "3  9  7  5  5  6  1  6\n",
       "4  7  7  1  8  1  7  8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4605ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dd820e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    9\n",
       "2    3\n",
       "3    6\n",
       "4    8\n",
       "Name: 6, dtype: int32"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0074a806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 3, 8])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,-1].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9461caf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 7, 5, 8])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[2].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a090dad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1\n",
       "6    1\n",
       "8    1\n",
       "1    1\n",
       "7    1\n",
       "Name: 5, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,-2].value_counts()   # 统计每个值出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0a435e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(['a', 'b', 'b', 'b', 'b', 'b', 'a', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ec59589f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    5\n",
       "a    2\n",
       "c    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7afa24",
   "metadata": {},
   "source": [
    "# 应用函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205944a5",
   "metadata": {},
   "source": [
    "## 成员关系判断"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5704bac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isin(['a','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b23fb61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8\n",
       "1    6\n",
       "2    6\n",
       "3    3\n",
       "4    6\n",
       "5    7\n",
       "6    6\n",
       "dtype: int32"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: x.max()-x.min(), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24250a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   0  1  2  3  4  5  6\n",
       "0  4  1  7  8  7  3  6\n",
       "1  1  5  4  5  6  6  9\n",
       "2  4  3  7  5  3  8  3\n",
       "3  9  7  5  5  6  1  6\n",
       "4  7  7  1  8  1  7  8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2c5b8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = list(\"abcdefg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bfcf4eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   a  b  c  d  e  f  g\n",
       "0  4  1  7  8  7  3  6\n",
       "1  1  5  4  5  6  6  9\n",
       "2  4  3  7  5  3  8  3\n",
       "3  9  7  5  5  6  1  6\n",
       "4  7  7  1  8  1  7  8"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ad7fcedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14\n",
       "1    11\n",
       "2    14\n",
       "3    19\n",
       "4    17\n",
       "Name: a, dtype: int64"
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.a.apply(lambda x: x+10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55449fd9",
   "metadata": {},
   "source": [
    "## 元素级应用函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d016b2f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>59</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
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       "      <th>4</th>\n",
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       "      <td>75</td>\n",
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      "text/plain": [
       "    a   b   c   d   e   f   g\n",
       "0  23   5  59  75  59  15  45\n",
       "1   5  33  23  33  45  45  93\n",
       "2  23  15  59  33  15  75  15\n",
       "3  93  59  33  33  45   5  45\n",
       "4  59  59   5  75   5  59  75"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.applymap(lambda x: x**2 + x + 3)  #和广播效果类似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ad3cdc53",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['g'] = ['ss','fge','sgs','gega','gas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "79e5ca8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>gega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>gas</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   a  b  c  d  e  f     g\n",
       "0  4  1  7  8  7  3    ss\n",
       "1  1  5  4  5  6  6   fge\n",
       "2  4  3  7  5  3  8   sgs\n",
       "3  9  7  5  5  6  1  gega\n",
       "4  7  7  1  8  1  7   gas"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7229d3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['g'] = data.g.apply(lambda x: x.title())  # g列字符串首字母大写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "92da00a6",
   "metadata": {},
   "outputs": [
    {
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       "<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>e</th>\n",
       "      <th>f</th>\n",
       "      <th>g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>Ss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Fge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>Sgs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Gega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Gas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d  e  f     g\n",
       "0  4  1  7  8  7  3    Ss\n",
       "1  1  5  4  5  6  6   Fge\n",
       "2  4  3  7  5  3  8   Sgs\n",
       "3  9  7  5  5  6  1  Gega\n",
       "4  7  7  1  8  1  7   Gas"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e55a821e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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