Pandas-DoNa.ipynb

{
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "回忆一下Pandas的运算特性。\n",
    "\n",
    "1.索引自动对齐，虽然索引顺序不一致，但是可以自动对齐。\n",
    "\n",
    "2.填充空的参数是fill_value=xxxx,例如：A.add(B,fill_value=xxx)\n",
    "\n",
    "3.axis = 0，代表沿着行对列操作。\n",
    "\n",
    "4.对pd进行运算后，还会保持住原来的索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas对空的处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.None是一个object，不是一个类型。  \n",
    "2.Pandas中的空类型为Nan(not a number)，是Float类型。  \n",
    "3.Pandas能自动将None转换为Nan  \n",
    "4.删除空值方法，dropna()  \n",
    "5.判断空值和非空方法isnull(),notnull()  \n",
    "6.dropna()会将含有空值的行和列都会删掉。  \n",
    "7.避免dropna()删掉全部空值，可指定删除行或列，方法为dropna(axis='columns') |||| dropna(axis = 'rows')，注意此处都是复数。  \n",
    "8.另外也可以删除符合条件的行或列，方法也是dropna()，但是要带另外一个参数，how='xxxx'.  \n",
    "&emsp;&emsp;删除带空值的行：dropna(axis = 'rows',how='any')  \n",
    "&emsp;&emsp;删除全为空的列：dropna(axis = 'columns',how = 'all')  \n",
    "9.可以通过非空的个数删除对应的行或列  \n",
    "&emsp;&emsp;删除空值等于3的行：dropna(axis = 'rows',thresh = 3)  \n",
    "10.填充空的方法是fillna().  \n",
    "&emsp;&emsp;填充固定值11：fillna(11).  \n",
    "&emsp;&emsp;根据前面值填充后面值：fillna(method='ffill') -- forward fill  \n",
    "&emsp;&emsp;&emsp;&emsp;沿着行对列进行填充：fillna(axis='rows',method='ffill')  \n",
    "&emsp;&emsp;根据后面值填充空值：fillna(method='bfill')  -- back fill  \n",
    "&emsp;&emsp;&emsp;&emsp;沿着列对行进行填充：fillna(axis='columns',method='bfill')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成一个pd\n",
    "\n",
    "data = pd.Series([1,None,np.nan,'hello'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "3    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna()"
   ]
  },
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   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1       22\n",
       "2       22\n",
       "3    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.fillna(22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1     None\n",
       "2      NaN\n",
       "3    hello\n",
       "dtype: object"
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     "execution_count": 11,
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   "source": [
    "data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([\n",
    "    [1,np.nan,22],\n",
    "    [3,None,11],\n",
    "    [55,66,88]\n",
    "])"
   ]
  },
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   "execution_count": 32,
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       "    0     1   2\n",
       "0   1   NaN  22\n",
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       "2  55  66.0  88"
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    "df"
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   "execution_count": 33,
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   "source": [
    "# 删除空,只要有空都会被删除\n",
    "df.dropna()"
   ]
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   "execution_count": 34,
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       "    0   2\n",
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   "source": [
    "# 使用how参数,删除有空的列\n",
    "df.dropna(how='any',axis='columns')"
   ]
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       "    0     1   2\n",
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       "2  55  66.0  88"
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   "source": [
    "# 删除全部为空的行\n",
    "df.dropna(how='all',axis='rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为没有整行都是空的行，所以没有任何数据被删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[3] = np.nan"
   ]
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   "cell_type": "code",
   "execution_count": 37,
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   "execution_count": 38,
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   "source": [
    "df.dropna(axis='columns',thresh=2)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "thresh参数代表大于和等于指定数据的对应行或列被删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
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       "    0      1   2      3\n",
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   "source": [
    "df.fillna(100)"
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