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## pandas简介
Pandas中一共有三种数据结构，分别为：Series、DataFrame和MultiIndex。
Python Data Analysis Library，面板数据（panel data）和python数据分析（data analysis）。最初由AQR Capital? Management于2008年4月开发，并于2009年底开源出来，目前由专注于Python数据包开发的PyData开发team继续开发和维护，属于PyData项目的一部分。Pandas最初被作为金融数据分析工具而开发出来，因此，pandas为时间序列分析提供了很好的支持。
{
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??? "import pandas as pd\n",
??? "import numpy as np"
?? ]
? },
? {
?? "cell_type": "code",
?? "execution_count": 8,
?? "id": "23c5a15f",
?? "metadata": {},
?? "outputs": [
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???? "name": "stdout",
???? "output_type": "stream",
???? "text": [
????? "0??? 0.959924\n",
????? "1??? 0.057490\n",
????? "2??? 0.395029\n",
????? "3??? 0.861239\n",
????? "4??? 0.332671\n",
????? "dtype: float64\n",
????? "0??? 0.959924\n",
????? "1??? 0.057490\n",
????? "2??? 0.395029\n",
????? "3??? 0.861239\n",
????? "dtype: float64\n",
????? "0??? 0.959924\n",
????? "1??? 0.057490\n",
????? "2??? 0.395029\n",
????? "3??? 0.861239\n",
????? "dtype: float64\n",
????? "0??? 0.959924\n",
????? "2??? 0.395029\n",
????? "4??? 0.332671\n",
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????? "0??? 20.000000\n",
????? "1??? 20.000000\n",
????? "2??? 20.000000\n",
????? "3???? 0.861239\n",
????? "4???? 0.332671\n",
????? "dtype: float64\n"
???? ]
??? }
?? ],
?? "source": [
??? "#切片索引\n",
??? "s=pd.Series(np.random.rand(5))\n",
??? "print(s)\n",
??? "print(s[0:4])\n",
??? "print(s[:-1])\n",
??? "print(s[::2])\n",
??? "#修改值\n",
??? "s[:-2]=20\n",
??? "print(s)\n"
?? ]
? },
? {
?? "cell_type": "code",
?? "execution_count": 45,
?? "id": "949b7461",
?? "metadata": {},
?? "outputs": [
??? {
???? "name": "stdout",
???? "output_type": "stream",
???? "text": [
????? "0??? 64.755105\n",
????? "1??? 50.714969\n",
????? "2??? 52.834138\n",
????? "3??? 89.628520\n",
????? "4??? 69.999119\n",
????? "dtype: float64\n",
????? "##############################\n",
????? "0??? 64.755105\n",
????? "1??? 50.714969\n",
????? "2??? 52.834138\n",
????? "3??? 89.628520\n",
????? "4????????? NaN\n",
????? "dtype: float64\n",
????? "##############################\n",
????? "0???? True\n",
????? "1??? False\n",
????? "2??? False\n",
????? "3???? True\n",
????? "4??? False\n",
????? "dtype: bool <class 'pandas.core.series.Series'>\n",
????? "##############################\n",
????? "1??? 50.714969\n",
????? "2??? 52.834138\n",
????? "dtype: float64\n",
????? "##############################\n",
????? "0??? False\n",
????? "1??? False\n",
????? "2??? False\n",
????? "3??? False\n",
????? "4???? True\n",
????? "dtype: bool\n",
????? "##############################\n",
????? "0???? True\n",
????? "1???? True\n",
????? "2???? True\n",
????? "3???? True\n",
????? "4??? False\n",
????? "dtype: bool\n",
????? "##############################\n",
????? "4?? NaN\n",
????? "dtype: float64\n"
???? ]
??? }
?? ],
?? "source": [
??? "#布尔索引\n",
??? "np.random.seed(88)\n",
??? "s=pd.Series(np.random.rand(5)*100)\n",
??? "print(s)\n",
??? "print('#'*30)\n",
??? "s[4]=None\n",
??? "print(s)\n",
??? "print('#'*30)\n",
??? "bol=s>55\n",
??? "print(bol,type(bol))\n",
??? "print('#'*30)\n",
??? "#通过布尔series获取值\n",
??? "print(s[s<55])\n",
??? "print('#'*30)\n",
??? "#查看series的方法\n",
??? "bol2=s.isnull()\n",
??? "print(bol2)\n",
??? "'''返回false没有缺失值返回true有缺失值 s.nonull()函数则相反'''\n",
??? "print('#'*30)\n",
??? "bol3=s.notnull()\n",
??? "print(bol3)\n",
??? "print('#'*30)\n",
??? "#获取空值\n",
??? "print(s[bol2])"
?? ]
? },
? {
?? "cell_type": "code",
?? "execution_count": 62,
?? "id": "6791fc89",
?? "metadata": {
??? "scrolled": true
?? },
?? "outputs": [
??? {
???? "name": "stdout",
???? "output_type": "stream",
???? "text": [
????? "0??? 64.755105\n",
????? "1??? 50.714969\n",
????? "2??? 52.834138\n",
????? "3??? 89.628520\n",
????? "4??? 69.999119\n",
????? "5??? 71.429710\n",
????? "6??? 71.733838\n",
????? "7??? 22.281946\n",
????? "8??? 17.515452\n",
????? "9??? 45.684149\n",
????? "dtype: float64\n",
????? "##############################\n",
????? "0??? 64.755105\n",
????? "dtype: float64\n",
????? "9??? 45.684149\n",
????? "dtype: float64\n"
???? ]
??? }
?? ],
?? "source": [
??? "#pandas数据结构series技巧----数据查看，重新索引，对齐，增，删，改\n",
??? "#数据查看\n",
??? "np.random.seed(88)\n",
??? "s=pd.Series(np.random.rand(10)*100)\n",
??? "print(s)\n",
??? "print('#'*30)\n",
??? "'''head,tail方法'''\n",
??? "print(s.head(1))\n",
??? "print(s.tail(1))\n",
??? "\n"
?? ]
? },
? {
?? "cell_type": "code",
?? "execution_count": 88,
?? "id": "c97af1c5",
?? "metadata": {},
?? "outputs": [
??? {
???? "name": "stdout",
???? "output_type": "stream",
???? "text": [
????? "0??? 64.755105\n",
????? "1??? 50.714969\n",
????? "2??? 52.834138\n",
????? "3??? 89.628520\n",
????? "4??? 69.999119\n",
????? "dtype: float64\n",
????? "##############################\n",
????? "c?? NaN\n",
????? "d?? NaN\n",
????? "a?? NaN\n",
????? "e?? NaN\n",
????? "f?? NaN\n",
????? "dtype: float64\n",
????? "c??? 11.000000\n",
????? "d??? 11.000000\n",
????? "a??? 11.000000\n",
????? "e??? 11.000000\n",
????? "f??? 11.000000\n",
????? "0??? 64.755105\n",
????? "1??? 50.714969\n",
????? "2??? 52.834138\n",
????? "3??? 89.628520\n",
????? "4??? 69.999119\n",
????? "5??? 11.000000\n",
????? "dtype: float64\n"
???? ]
??? }
?? ],
?? "source": [
??? "#重新索引\n",
??? "np.random.seed(88)\n",
??? "s=pd.Series(np.random.rand(5)*100)\n",
??? "print(s)\n",
??? "print('#'*30)\n",
??? "'''重新索引后面value为null'''\n",
??? "s1=s.reindex(['c','d','a','e','f'])\n",
??? "print(s1)\n",
??? "s2=s.reindex(['c','d','a','e','f',0,1,2,3,4,5],fill_value=11)\n",
??? "print(s2)"
?? ]
? },
? {
?? "cell_type": "code",
?? "execution_count": 94,
?? "id": "43356734",
?? "metadata": {},
?? "outputs": [
??? {
???? "name": "stdout",
???? "output_type": "stream",
???? "text": [
????? "a??? 64.755105\n",
????? "b??? 50.714969\n",
????? "c??? 52.834138\n",
????? "dtype: float64\n",
????? "##############################\n",
????? "a??? 89.628520\n",
????? "e??? 69.999119\n",
????? "f??? 71.429710\n",
????? "dtype: float64\n",
????? "##############################\n",
????? "a??? 154.383625\n",
????? "b?????????? NaN\n",
????? "c?????????? NaN\n",
????? "e?????????? NaN\n",
????? "f?????????? NaN\n",
????? "dtype: float64\n"
???? ]
??? }
?? ],
?? "source": [
??? "#对齐\n",
??? "np.random.seed(88)\n",
??? "s1=pd.Series(np.random.rand(3)*100,index=['a','b','c'])\n",
??? "s2=pd.Series(np.random.rand(3)*100,index=['a','e','f'])\n",
??? "print(s1)\n",
??? "print('#'*30)\n",
??? "print(s2)\n",
??? "print('#'*30)\n",
??? "print(s1+s2)"
?? ]
? },
? {
?? "cell_type": "code",
?? "execution_count": null,
?? "id": "faa8a408",
?? "metadata": {},
?? "outputs": [],
?? "source": []
? },
? {
?? "cell_type": "code",
?? "execution_count": null,
?? "id": "e32f916a",
?? "metadata": {},
?? "outputs": [],
?? "source": []
? },
? {
?? "cell_type": "code",
?? "execution_count": null,
?? "id": "1d54af1d",
?? "metadata": {},
?? "outputs": [],
?? "source": []
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?? "execution_count": null,
?? "id": "fa8f3c3a",
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?? "cell_type": "code",
?? "execution_count": null,
?? "id": "e5e09818",
?? "metadata": {},
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?? "source": []
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?? "cell_type": "code",
?? "execution_count": null,
?? "id": "8fdfb24a",
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