15 处理缺失值.ipynb

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       "0    1.0\n",
       "1    NaN\n",
       "2    3.5\n",
       "3    NaN\n",
       "4    7.0\n",
       "dtype: float64"
      ]
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     "execution_count": 10,
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import nan as NA\n",
    "\n",
    "data = pd.Series([1, NA, 3.5, NA, 7])\n",
    "data"
   ]
  },
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   "cell_type": "code",
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "data.dropna()"
   ]
  },
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       "     0    1    2\n",
       "0  1.0  6.5  3.0\n",
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   ],
   "source": [
    "# 对于DataFrame对象。dropna默认丢弃任何含有缺失值的行：\n",
    "\n",
    "data = pd.DataFrame([[1., 6.5, 3.], [1., NA, NA],\n",
    "                     [NA, NA, NA], [NA, 6.5, 3.]])\n",
    "data"
   ]
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   "source": [
    "cleaned = data.dropna()\n",
    "cleaned"
   ]
  },
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   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "     0    1    2\n",
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   "source": [
    "# 传入how='all'将只丢弃全为NA的那些行：\n",
    "\n",
    "data.dropna(how='all')"
   ]
  },
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    "# 用这种方式丢弃列，只需传入axis=1即可：\n",
    "\n",
    "data[4] = NA\n",
    "data"
   ]
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    "data.dropna(axis=1, how='all')"
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       "          0         1         2\n",
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   "source": [
    "# thresh参数\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(7, 3))\n",
    "df.iloc[:4, 1] = NA\n",
    "df.iloc[:2, 2] = NA\n",
    "df"
   ]
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       "      <td>0.360234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.560420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.093655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.778666</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.980662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.102276</td>\n",
       "      <td>-0.731845</td>\n",
       "      <td>0.535380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.737745</td>\n",
       "      <td>-0.642394</td>\n",
       "      <td>-0.943436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.621591</td>\n",
       "      <td>-1.120598</td>\n",
       "      <td>-0.459968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.355704  0.000000  0.000000\n",
       "1  0.360234  0.000000  0.000000\n",
       "2  0.560420  0.000000 -0.093655\n",
       "3  0.778666  0.000000  1.980662\n",
       "4  0.102276 -0.731845  0.535380\n",
       "5  0.737745 -0.642394 -0.943436\n",
       "6  0.621591 -1.120598 -0.459968"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 填充缺失数据\n",
    "\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.500000</td>\n",
       "      <td>-0.093655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.778666</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.980662</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.102276</td>\n",
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       "      <td>0.535380</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.737745</td>\n",
       "      <td>-0.642394</td>\n",
       "      <td>-0.943436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.621591</td>\n",
       "      <td>-1.120598</td>\n",
       "      <td>-0.459968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -0.355704  0.500000  0.000000\n",
       "1  0.360234  0.500000  0.000000\n",
       "2  0.560420  0.500000 -0.093655\n",
       "3  0.778666  0.500000  1.980662\n",
       "4  0.102276 -0.731845  0.535380\n",
       "5  0.737745 -0.642394 -0.943436\n",
       "6  0.621591 -1.120598 -0.459968"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过一个字典调用fillna，就可以实现对不同的列填充不同的值：\n",
    "\n",
    "df.fillna({1: 0.5, 2: 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>-1.823105</td>\n",
       "      <td>-0.458591</td>\n",
       "      <td>-0.462004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.037358</td>\n",
       "      <td>0.579737</td>\n",
       "      <td>-0.815568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.053422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.916389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.509507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.487497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.180417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.275768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0 -1.823105 -0.458591 -0.462004\n",
       "1  2.037358  0.579737 -0.815568\n",
       "2 -0.053422       NaN  1.916389\n",
       "3  0.509507       NaN -0.487497\n",
       "4  2.180417       NaN       NaN\n",
       "5  1.275768       NaN       NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ffill\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(6, 3))\n",
    "df.iloc[2:, 1] = NA\n",
    "df.iloc[4:, 2] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
       "      <td>1.275768</td>\n",
       "      <td>0.579737</td>\n",
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       "          0         1         2\n",
       "0 -1.823105 -0.458591 -0.462004\n",
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       "4  2.180417  0.579737 -0.487497\n",
       "5  1.275768  0.579737 -0.487497"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>k2</th>\n",
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       "      <td>two</td>\n",
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       "      <th>5</th>\n",
       "      <td>two</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  two   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  one   3\n",
       "5  two   4\n",
       "6  two   4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除重复数据\n",
    "\n",
    "data = pd.DataFrame({'k1': ['one', 'two'] * 3 + ['two'],\n",
    "                         'k2': [1, 1, 2, 3, 3, 4, 4]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用duplicated()查看是否有重复项\n",
    "\n",
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  two   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  one   3\n",
       "5  two   4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop_duplicates()删除重复项\n",
    "\n",
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "      <th>v1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "    k1  k2  v1\n",
       "0  one   1   0\n",
       "1  two   1   1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只希望通过某一列删除重复项\n",
    "\n",
    "data['v1'] = range(7)\n",
    "data.drop_duplicates(['k1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2  v1\n",
       "0  one   1   0\n",
       "1  two   1   1\n",
       "2  one   2   2\n",
       "3  two   3   3\n",
       "4  one   3   4\n",
       "6  two   4   6"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认保留的是第一个出现的值组合。传入keep='last'则保留最后一个：\n",
    "\n",
    "data.drop_duplicates(['k1', 'k2'], keep='last')"
   ]
  }
 ],
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