4.2 pandas 数据清理.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e32f1694",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5b423c0",
   "metadata": {},
   "source": [
    "# 重复值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "144cafd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'k1':['one']*3 + ['two']*2, 'k2':[1,1,2,3,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "575cfa79",
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   "outputs": [
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       "  </thead>\n",
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      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  one   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  two   3"
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79bc7aa0",
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    {
     "data": {
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       "2"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "(data.duplicated()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6531a5f",
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   "outputs": [
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       "    k1  k2\n",
       "0  one   1\n",
       "2  one   2\n",
       "3  two   3"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e851247",
   "metadata": {},
   "outputs": [
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       "    k1  k2\n",
       "0  one   1\n",
       "3  two   3"
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     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "data.drop_duplicates('k1')  # 根据k1进行去重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a728f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['k3'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "025307e6",
   "metadata": {},
   "outputs": [
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       "    k1  k2  k3\n",
       "0  one   1   1\n",
       "1  one   1   1\n",
       "2  one   2   1\n",
       "3  two   3   1\n",
       "4  two   3   1"
      ]
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     "execution_count": 10,
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    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ed16647",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "      <th>k3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
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      "text/plain": [
       "    k1  k2  k3\n",
       "0  one   1   1\n",
       "3  two   3   1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(['k1', 'k3'])  # 根据k1,k3进行去重"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe7a099",
   "metadata": {},
   "source": [
    "# 数值替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f8aad2ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      one\n",
       "1      one\n",
       "2      one\n",
       "3    three\n",
       "4    three\n",
       "Name: k1, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.k1.replace('two','three')  # k1列，two替换成three（对Series）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc0392b0",
   "metadata": {},
   "outputs": [
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       "    k1  k2  k3\n",
       "0  one   1   1\n",
       "1  one   1   1\n",
       "2  one   2   1\n",
       "3  two   3   1\n",
       "4  two   3   1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "333b83ab",
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   "outputs": [
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       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "      <th>k3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
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      "text/plain": [
       "    k1   k2   k3\n",
       "0  one  100  100\n",
       "1  one  100  100\n",
       "2  one    2  100\n",
       "3  two    3  100\n",
       "4  two    3  100"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace(1, 100) # 对DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6b654fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace(1, 100, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "60a87619",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>k3</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
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      "text/plain": [
       "    k1   k2   k3\n",
       "0  one  100  100\n",
       "1  one  100  100\n",
       "2  one    2  100\n",
       "3  two    3  100\n",
       "4  two    3  100"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0d5f86",
   "metadata": {},
   "source": [
    "# 过滤缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8ff0e6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([2, np.nan, 4, np.nan, 8.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f127a534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    NaN\n",
       "2    4.0\n",
       "3    NaN\n",
       "4    8.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c1944a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data.isnull()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2f29b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.notnull().sum()  # 不加括号也可以"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "787c57b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "2    4.0\n",
       "4    8.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a85582ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)  # 抛弃nan值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a48cffb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "2    4.0\n",
       "4    8.5\n",
       "dtype: float64"
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d7c231a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.random.randn(4,3),index=list('abcd'),columns=['aa','bb','cc'])\n",
    "data.iloc[1:,:2] = np.nan\n",
    "data.iloc[1,2] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48dcd88a",
   "metadata": {},
   "outputs": [
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   "execution_count": 32,
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   "outputs": [
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     "metadata": {},
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  {
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   "execution_count": 33,
   "id": "91f24b48",
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   "outputs": [
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       "         aa        bb       cc\n",
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     "execution_count": 33,
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  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bbb6ca0a",
   "metadata": {},
   "outputs": [
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     "execution_count": 34,
     "metadata": {},
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   "source": [
    "data.dropna(how='all')  # 全部为nan，才会被抛弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a16d4ed9",
   "metadata": {},
   "outputs": [
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       "         aa        bb        cc\n",
       "a -0.474847 -0.398699 -0.432810\n",
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       "d       NaN       NaN -0.557477"
      ]
     },
     "execution_count": 35,
     "metadata": {},
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   "source": [
    "data"
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  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1ca37dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.iloc[0, 0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "568f63b3",
   "metadata": {},
   "outputs": [
    {
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       "   aa        bb        cc\n",
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     "execution_count": 37,
     "metadata": {},
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    "data"
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  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0be976f2",
   "metadata": {},
   "outputs": [
    {
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       "   aa        bb        cc\n",
       "a NaN -0.398699 -0.432810\n",
       "c NaN       NaN  2.835789\n",
       "d NaN       NaN -0.557477"
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     "execution_count": 38,
     "metadata": {},
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   "source": [
    "data.dropna(axis=0, how='all')  # axis=0，根据行判断，全部为nan，才会被抛弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "158fd2d6",
   "metadata": {},
   "outputs": [
    {
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       "         bb        cc\n",
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     "execution_count": 39,
     "metadata": {},
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   "source": [
    "data.dropna(axis=1, how='all')  # axis=1，根据列判断，全部为nan，才会被抛弃"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f721a7",
   "metadata": {},
   "source": [
    "# 填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a33cb3e9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>c</th>\n",
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       "   aa        bb        cc\n",
       "a NaN -0.398699 -0.432810\n",
       "b NaN       NaN       NaN\n",
       "c NaN       NaN  2.835789\n",
       "d NaN       NaN -0.557477"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fab2aa8d",
   "metadata": {},
   "outputs": [
    {
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       "\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>aa</th>\n",
       "      <th>bb</th>\n",
       "      <th>cc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.398699</td>\n",
       "      <td>-0.432810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.835789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.557477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    aa        bb        cc\n",
       "a  0.0 -0.398699 -0.432810\n",
       "b  0.0  0.000000  0.000000\n",
       "c  0.0  0.000000  2.835789\n",
       "d  0.0  0.000000 -0.557477"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(0) # nan值填充为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cb591253",
   "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>aa</th>\n",
       "      <th>bb</th>\n",
       "      <th>cc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.398699</td>\n",
       "      <td>-0.432810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.835789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>-0.557477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    aa          bb        cc\n",
       "a  1.0   -0.398699 -0.432810\n",
       "b  1.0  100.000000       NaN\n",
       "c  1.0  100.000000  2.835789\n",
       "d  1.0  100.000000 -0.557477"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna({'aa':1, 'bb':100})  #aa填充1，bb填充100，cc不填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ad8582a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series([2, np.nan, 4, np.nan, 8.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "61252ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    NaN\n",
       "2    4.0\n",
       "3    NaN\n",
       "4    8.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ad3eedcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.000000\n",
       "1    4.833333\n",
       "2    4.000000\n",
       "3    4.833333\n",
       "4    8.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(data.mean())  # 使用均值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61553d56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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