Pandas用法笔记总结 （详细）
| 目录 Pandas 创建对象 可视化数据 Geting数据 赋值 缺失数据处理 数据操作 字符串方法 查询聚合 重塑（reshape） 透视表（Pivot table） 时间序列（Time Series） 画图 Categoricals 文件输入输出获取数据（Getting Data In/Out） csv HDF5 Excel python-获取当前工作路径 |
Pandas这是一个简短的介绍pandas用法，主要面向新用户。 在Cookbook你可以看到更复杂的方法。 通常，我们导入以下模块： In [1]: import pandas as pd In [2]: import numpy as np In [3]: import matplotlib.pyplot as plt 创建对象创建一个Series对象： In [4]: s = pd.Series([1,3,5,np.nan,6,8]) In [5]: s Out[5]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 通过numpy数组创建一个DateFrame对象，包括索引和列标签： In [6]: dates = pd.date_range('20130101', periods=6) In [7]: dates Out[7]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D')
In [8]: df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
In [9]: df Out[9]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 通过字典方式创建DataFrame对象： In [10]: df2 = pd.DataFrame({ 'A' : 1., ....: 'B' : pd.Timestamp('20130102'), ....: 'C' : pd.Series(1,index=list(range(4)),dtype='float32'), ....: 'D' : np.array([3] * 4,dtype='int32'), ....: 'E' : pd.Categorical(["test","train","test","train"]), ....: 'F' : 'foo' }) ....:
In [11]: df2 Out[11]: A B C D E F 0 1.0 2013-01-02 1.0 3 test foo 1 1.0 2013-01-02 1.0 3 train foo 2 1.0 2013-01-02 1.0 3 test foo 3 1.0 2013-01-02 1.0 3 train foo 查看各列的类型： In [12]: df2.dtypes Out[12]: A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object 如果使用IPython，带有自动补全功能，可以利用tab键查看所有的列的名字和公有属性 In [13]: df2. df2.A df2.boxplot df2.abs df2.C df2.add df2.clip df2.add_prefix df2.clip_lower df2.add_suffix df2.clip_upper df2.align df2.columns df2.all df2.combine df2.any df2.combineAdd df2.append df2.combine_first df2.apply df2.combineMult df2.applymap df2.compound df2.as_blocks df2.consolidate df2.asfreq df2.convert_objects df2.as_matrix df2.copy df2.astype df2.corr df2.at df2.corrwith df2.at_time df2.count df2.axes df2.cov df2.B df2.cummax df2.between_time df2.cummin df2.bfill df2.cumprod df2.blocks df2.cumsum df2.bool df2.D 可视化数据查看首尾行数： In [14]: df.head() Out[14]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401
In [15]: df.tail(3) Out[15]: A B C D 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 显示索引，列标签和底层numpy数据： In [16]: df.index Out[16]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D')
In [17]: df.columns Out[17]: Index([u'A', u'B', u'C', u'D'], dtype='object')
In [18]: df.values Out[18]: array([[ 0.4691, -0.2829, -1.5091, -1.1356], [ 1.2121, -0.1732, 0.1192, -1.0442], [-0.8618, -2.1046, -0.4949, 1.0718], [ 0.7216, -0.7068, -1.0396, 0.2719], [-0.425 , 0.567 , 0.2762, -1.0874], [-0.6737, 0.1136, -1.4784, 0.525 ]]) describe方法显示数据的快速统计汇总结果： In [19]: df.describe() Out[19]: A B C D count 6.000000 6.000000 6.000000 6.000000 mean 0.073711 -0.431125 -0.687758 -0.233103 std 0.843157 0.922818 0.779887 0.973118 min -0.861849 -2.104569 -1.509059 -1.135632 25% -0.611510 -0.600794 -1.368714 -1.076610 50% 0.022070 -0.228039 -0.767252 -0.386188 75% 0.658444 0.041933 -0.034326 0.461706 max 1.212112 0.567020 0.276232 1.071804 转置数据： In [20]: df.T Out[20]: 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06 A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690 B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648 C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427 D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988 按索引排序： In [21]: df.sort_index(axis=1, ascending=False) Out[21]: D C B A 2013-01-01 -1.135632 -1.509059 -0.282863 0.469112 2013-01-02 -1.044236 0.119209 -0.173215 1.212112 2013-01-03 1.071804 -0.494929 -2.104569 -0.861849 2013-01-04 0.271860 -1.039575 -0.706771 0.721555 2013-01-05 -1.087401 0.276232 0.567020 -0.424972 2013-01-06 0.524988 -1.478427 0.113648 -0.673690 按指定列的值排序： In [22]: df.sort_values(by='B') Out[22]: A B C D 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 选择数据 Note：标准Python/Numpy的数据选择和设置很直观和方便，但是在生产环境，我们推荐优化的pandas方法，如at, .iat, .loc, .iloc 和 .ix Geting数据选择一列数据，返回Series数据类型，和 df.A 命令等价： In [23]: df['A'] Out[23]: 2013-01-01 0.469112 2013-01-02 1.212112 2013-01-03 -0.861849 2013-01-04 0.721555 2013-01-05 -0.424972 2013-01-06 -0.673690 Freq: D, Name: A, dtype: float64 通过 [] 选择行数据： In [24]: df[0:3] Out[24]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
In [25]: df['20130102':'20130104'] Out[25]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 列标签选择数据 通过date索引获取一个横截面（cross section）数据： In [26]: df.loc[dates[0]] Out[26]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float64 多个标签获取数据： In [27]: df.loc[:,['A','B']] Out[27]: A B 2013-01-01 0.469112 -0.282863 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 2013-01-06 -0.673690 0.113648 切片数据： In [28]: df.loc['20130102':'20130104',['A','B']] Out[28]: A B 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 在切片数据上精简维度： In [29]: df.loc['20130102',['A','B']] Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64 获取一个标量数据： In [30]: df.loc[dates[0],'A'] Out[30]: 0.46911229990718628 一个更快速获取标量数据的方法（和上一个方法等同）： In [31]: df.at[dates[0],'A'] Out[31]: 0.46911229990718628 通过位置获取数据 通过传递一个整数值定位： In [32]: df.iloc[3] Out[32]: A 0.721555 B -0.706771 C -1.039575 D 0.271860 Name: 2013-01-04 00:00:00, dtype: float64 类似Numpy/python，通过切片定位： In [33]: df.iloc[3:5,0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 通过整数列表定位： In [34]: df.iloc[[1,2,4],[0,2]] Out[34]: A C 2013-01-02 1.212112 0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972 0.276232 指定行切片： In [35]: df.iloc[1:3,:] Out[35]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 指定列切片： In [36]: df.iloc[:,1:3] Out[36]: B C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215 0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05 0.567020 0.276232 2013-01-06 0.113648 -1.478427 通过位置获取值： In [37]: df.iloc[1,1] Out[37]: -0.17321464905330858 类似的iat方法： In [38]: df.iat[1,1] Out[38]: -0.17321464905330858 布尔索引 通过单列值取数： In [39]: df[df.A > 0] Out[39]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 一个where操作取值： In [40]: df[df > 0] Out[40]: A B C D 2013-01-01 0.469112 NaN NaN NaN 2013-01-02 1.212112 NaN 0.119209 NaN 2013-01-03 NaN NaN NaN 1.071804 2013-01-04 0.721555 NaN NaN 0.271860 2013-01-05 NaN 0.567020 0.276232 NaN 2013-01-06 NaN 0.113648 NaN 0.524988 isin（）方法： In [41]: df2 = df.copy()
In [42]: df2['E'] = ['one', 'one','two','three','four','three']
In [43]: df2 Out[43]: A B C D E 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three
In [44]: df2[df2['E'].isin(['two','four'])] Out[44]: A B C D E 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 赋值相同索引赋值一列数据： In [45]: s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64
In [47]: df['F'] = s1 通过标签赋值： In [48]: df.at[dates[0],'A'] = 0
位置赋值： In [49]: df.iat[0,1] = 0 numpy数组赋值： In [50]: df.loc[:,'D'] = np.array([5] * len(df)) 前面操作的结果展示： In [51]: df Out[51]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 2013-01-05 -0.424972 0.567020 0.276232 5 4.0 2013-01-06 -0.673690 0.113648 -1.478427 5 5.0 where操作赋值： In [52]: df2 = df.copy()
In [53]: df2[df2 > 0] = -df2
In [54]: df2 Out[54]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 -5 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0 2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0 2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0 2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0 2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0 缺失数据处理pandas 主要用np.nan表示缺失数据，默认不列入计算。
reindex方法允许在指定的轴上增/删/改原索引，返回一个副本： In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
In [56]: df1.loc[dates[0]:dates[1],'E'] = 1
In [57]: df1 Out[57]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN
删除有缺失值的行： In [58]: df1.dropna(how='any') Out[58]: A B C D F E 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
在缺失值位置填充： In [59]: df1.fillna(value=5) Out[59]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0
判断是否缺失，返回布尔集： In [60]: pd.isnull(df1) Out[60]: A B C D F E 2013-01-01 False False False False True False 2013-01-02 False False False False False False 2013-01-03 False False False False False True 2013-01-04 False False False False False True 数据操作Operations 通常排除缺失数据 描述统计： In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float64 同样操作在标签维度： In [62]: df.mean(1) Out[62]: 2013-01-01 0.872735 2013-01-02 1.431621 2013-01-03 0.707731 2013-01-04 1.395042 2013-01-05 1.883656 2013-01-06 1.592306 Freq: D, dtype: float64 pandas操作不同维度的数据需要对齐，另外它会按指定的维度方向计算： In [63]: s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
In [64]: s Out[64]: 2013-01-01 NaN 2013-01-02 NaN 2013-01-03 1.0 2013-01-04 3.0 2013-01-05 5.0 2013-01-06 NaN Freq: D, dtype: float64
In [65]: df.sub(s, axis='index') Out[65]: A B C D F 2013-01-01 NaN NaN NaN NaN NaN 2013-01-02 NaN NaN NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0 2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0 2013-01-06 NaN NaN NaN NaN NaN
apply方法 applying 函数： In [66]: df.apply(np.cumsum) Out[66]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 -1.389850 10 1.0 2013-01-03 0.350263 -2.277784 -1.884779 15 3.0 2013-01-04 1.071818 -2.984555 -2.924354 20 6.0 2013-01-05 0.646846 -2.417535 -2.648122 25 10.0 2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0
In [67]: df.apply(lambda x: x.max() - x.min()) Out[67]: A 2.073961 B 2.671590 C 1.785291 D 0.000000 F 4.000000 dtype: float64 直方图 In [68]: s = pd.Series(np.random.randint(0, 7, size=10))
In [69]: s Out[69]: 0 4 1 2 2 1 3 2 4 6 5 4 6 4 7 6 8 4 9 4 dtype: int64
In [70]: s.value_counts() Out[70]: 4 5 6 2 2 2 1 1 dtype: int64 字符串方法Series中的字符处理方法和Python中的str方法一样。另外str方法默认在模式匹配的时候默认使用正则表达。 In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object 查询聚合合并(merge) concat方法： In [73]: df = pd.DataFrame(np.random.randn(10, 4))
In [74]: df Out[74]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495
# break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]]
In [76]: pd.concat(pieces) Out[76]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 join方法 In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
In [79]: left Out[79]: key lval 0 foo 1 1 foo 2
In [80]: right Out[80]: key rval 0 foo 4 1 foo 5
In [81]: pd.merge(left, right, on='key') Out[81]: key lval rval 0 foo 1 4 1 foo 1 5 2 foo 2 4 3 foo 2 5 append方法 在DataFrame中增加一列： In [82]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
In [83]: df Out[83]: A B C D 0 1.346061 1.511763 1.627081 -0.990582 1 -0.441652 1.211526 0.268520 0.024580 2 -1.577585 0.396823 -0.105381 -0.532532 3 1.453749 1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346 0.339969 -0.693205 5 -0.339355 0.593616 0.884345 1.591431 6 0.141809 0.220390 0.435589 0.192451 7 -0.096701 0.803351 1.715071 -0.708758
In [84]: s = df.iloc[3]
In [85]: df.append(s, ignore_index=True) Out[85]: A B C D 0 1.346061 1.511763 1.627081 -0.990582 1 -0.441652 1.211526 0.268520 0.024580 2 -1.577585 0.396823 -0.105381 -0.532532 3 1.453749 1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346 0.339969 -0.693205 5 -0.339355 0.593616 0.884345 1.591431 6 0.141809 0.220390 0.435589 0.192451 7 -0.096701 0.803351 1.715071 -0.708758 分组（grouping） 在”group by”的时候涉及到以下几步： Spliting 按条件分割数据 Applying 在每组上应用函数 Combing 合并成一个数据集 In [86]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ....: 'foo', 'bar', 'foo', 'foo'], ....: 'B' : ['one', 'one', 'two', 'three', ....: 'two', 'two', 'one', 'three'], ....: 'C' : np.random.randn(8), ....: 'D' : np.random.randn(8)}) ....:
In [87]: df Out[87]: A B C D 0 foo one -1.202872 -0.055224 1 bar one -1.814470 2.395985 2 foo two 1.018601 1.552825 3 bar three -0.595447 0.166599 4 foo two 1.395433 0.047609 5 bar two -0.392670 -0.136473 6 foo one 0.007207 -0.561757 7 foo three 1.928123 -1.623033 先分组然后应用sum函数： In [88]: df.groupby('A').sum() Out[88]: C D A bar -2.802588 2.42611 foo 3.146492 -0.63958
通过多列分组并生成层次索引，然后应用函数： In [89]: df.groupby(['A','B']).sum() Out[89]: C D A B bar one -1.814470 2.395985 three -0.595447 0.166599 two -0.392670 -0.136473 foo one -1.195665 -0.616981 three 1.928123 -1.623033 two 2.414034 1.600434 重塑（reshape）stack方法 In [90]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', ....: 'foo', 'foo', 'qux', 'qux'], ....: ['one', 'two', 'one', 'two', ....: 'one', 'two', 'one', 'two']])) ....:
In [91]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
In [92]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
In [93]: df2 = df[:4]
In [94]: df2 Out[94]: A B first second bar one 0.029399 -0.542108 two 0.282696 -0.087302 baz one -1.575170 1.771208 two 0.816482 1.100230 stack方法用列标签新增一层索引： In [95]: stacked = df2.stack()
In [96]: stacked Out[96]: first second bar one A 0.029399 B -0.542108 two A 0.282696 B -0.087302 baz one A -1.575170 B 1.771208 two A 0.816482 B 1.100230 dtype: float64 stack方法的逆操作为unstack，默认解压最后一层： In [97]: stacked.unstack() Out[97]: A B first second bar one 0.029399 -0.542108 two 0.282696 -0.087302 baz one -1.575170 1.771208 two 0.816482 1.100230
In [98]: stacked.unstack(1) Out[98]: second one two first bar A 0.029399 0.282696 B -0.542108 -0.087302 baz A -1.575170 0.816482 B 1.771208 1.100230
In [99]: stacked.unstack(0) Out[99]: first bar baz second one A 0.029399 -1.575170 B -0.542108 1.771208 two A 0.282696 0.816482 B -0.087302 1.100230 透视表（Pivot table）In [100]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3, .....: 'B' : ['A', 'B', 'C'] * 4, .....: 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2, .....: 'D' : np.random.randn(12), .....: 'E' : np.random.randn(12)}) .....:
In [101]: df Out[101]: A B C D E 0 one A foo 1.418757 -0.179666 1 one B foo -1.879024 1.291836 2 two C foo 0.536826 -0.009614 3 three A bar 1.006160 0.392149 4 one B bar -0.029716 0.264599 5 one C bar -1.146178 -0.057409 6 two A foo 0.100900 -1.425638 7 three B foo -1.035018 1.024098 8 one C foo 0.314665 -0.106062 9 one A bar -0.773723 1.824375 10 two B bar -1.170653 0.595974 11 three C bar 0.648740 1.167115 可以通过pivot_table方法很轻松的透视数据： In [102]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C']) Out[102]: C bar foo A B one A -0.773723 1.418757 B -0.029716 -1.879024 C -1.146178 0.314665 three A 1.006160 NaN B NaN -1.035018 C 0.648740 NaN two A NaN 0.100900 B -1.170653 NaN C NaN 0.536826 时间序列（Time Series）pandas 拥有简单，强大，高效的函数用来处理频率转换中的重采样问题（例如将秒数据转换为5分钟数据）。
In [103]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
In [104]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [105]: ts.resample('5Min').sum() Out[105]: 2012-01-01 25083 Freq: 5T, dtype: int64 时区表示： In [106]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D') In [107]: ts = pd.Series(np.random.randn(len(rng)), rng) In [108]: ts Out[108]: 2012-03-06 0.464000 2012-03-07 0.227371 2012-03-08 -0.496922 2012-03-09 0.306389 2012-03-10 -2.290613 Freq: D, dtype: float64 In [109]: ts_utc = ts.tz_localize('UTC') In [110]: ts_utc Out[110]: 2012-03-06 00:00:00+00:00 0.464000 2012-03-07 00:00:00+00:00 0.227371 2012-03-08 00:00:00+00:00 -0.496922 2012-03-09 00:00:00+00:00 0.306389 2012-03-10 00:00:00+00:00 -2.290613 Freq: D, dtype: float64 转换时区： In [111]: ts_utc.tz_convert('US/Eastern') Out[111]: 2012-03-05 19:00:00-05:00 0.464000 2012-03-06 19:00:00-05:00 0.227371 2012-03-07 19:00:00-05:00 -0.496922 2012-03-08 19:00:00-05:00 0.306389 2012-03-09 19:00:00-05:00 -2.290613 Freq: D, dtype: float64 时区跨度转换： In [112]: rng = pd.date_range('1/1/2012', periods=5, freq='M') In [113]: ts = pd.Series(np.random.randn(len(rng)), index=rng) In [114]: ts Out[114]: 2012-01-31 -1.134623 2012-02-29 -1.561819 2012-03-31 -0.260838 2012-04-30 0.281957 2012-05-31 1.523962 Freq: M, dtype: float64 In [115]: ps = ts.to_period() In [116]: ps Out[116]: 2012-01 -1.134623 2012-02 -1.561819 2012-03 -0.260838 2012-04 0.281957 2012-05 1.523962 Freq: M, dtype: float64
In [117]: ps.to_timestamp() Out[117]: 2012-01-01 -1.134623 2012-02-01 -1.561819 2012-03-01 -0.260838 2012-04-01 0.281957 2012-05-01 1.523962 Freq: MS, dtype: float64 period和timestamp之间的转换让某些算术函数应用起来非常方便。下面的例子将一个quarterly frequency with year ending in November 转化成 9am of the end of the month following the quarter end: In [118]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV') In [119]: ts = pd.Series(np.random.randn(len(prng)), prng) In [120]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9 In [121]: ts.head() Out[121]: 1990-03-01 09:00 -0.902937 1990-06-01 09:00 0.068159 1990-09-01 09:00 -0.057873 1990-12-01 09:00 -0.368204 1991-03-01 09:00 -1.144073 Freq: H, dtype: float64 画图In [130]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
In [131]: ts = ts.cumsum()
In [132]: ts.plot() Out[132]: 在DataFrame中画出所有列： In [133]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, .....: columns=['A', 'B', 'C', 'D']) .....:
In [134]: df = df.cumsum()
In [135]: plt.figure(); df.plot(); plt.legend(loc='best') Out[135]:
Categoricals从0.15版开始，DateFrame已经包含了categorical类型 将原始数据转换为categorical类型： In [123]: df["grade"] = df["raw_grade"].astype("category") In [124]: df["grade"] Out[124]: 0 a 1 b 2 b 3 a 4 a 5 e Name: grade, dtype: category Categories (3, object): [a, b, e] 重命名categorical类型： In [125]: df["grade"].cat.categories = ["very good","good","very bad"] 重新排列并新增缺失数据： In [126]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
In [127]: df["grade"] Out[127]: 0 very good 1 good 2 good 3 very good 4 very good 5 very bad Name: grade, dtype: category Categories (5, object): [very bad, bad, medium, good, very good] 排序： In [128]: df.sort_values(by="grade") Out[128]: id raw_grade grade 5 6 e very bad 1 2 b good 2 3 b good 0 1 a very good 3 4 a very good 4 5 a very good 分组： In [129]: df.groupby("grade").size() Out[129]: grade very bad 1 bad 0 medium 0 good 2 very good 3 dtype: int64 文件输入输出获取数据（Getting Data In/Out）csv将数据写入一个csv文件： In [136]: df.to_csv('foo.csv') 读取csv数据文件： In [137]: pd.read_csv('foo.csv') Out[137]: Unnamed: 0 A B C D 0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 4 2000-01-05 0.578117 0.511371 0.103552 -2.428202 5 2000-01-06 0.478344 0.449933 -0.741620 -1.962409 6 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753 .. ... ... ... ... ... 993 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940 994 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107 995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368
[1000 rows x 5 columns] HDF5写入HDF5： In [138]: df.to_hdf('foo.h5','df') 读取HDF5文件： In [139]: pd.read_hdf('foo.h5','df') Out[139]: A B C D 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 2000-01-05 0.578117 0.511371 0.103552 -2.428202 2000-01-06 0.478344 0.449933 -0.741620 -1.962409 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753 ... ... ... ... ... 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368
[1000 rows x 4 columns] Excel写入excel： In [140]: df.to_excel('foo.xlsx', sheet_name='Sheet1')
读取Excel： In [141]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']) Out[141]: A B C D 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 2000-01-05 0.578117 0.511371 0.103552 -2.428202 2000-01-06 0.478344 0.449933 -0.741620 -1.962409 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753 ... ... ... ... ... 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368
[1000 rows x 4 columns] python-获取当前工作路径
#获得的是当前执行脚本的位置（若在命令行执行的该命令，则为空） 运行结果（在python脚本中执行的结果）： F:/SEG/myResearch/myProject_2/test.py
运行结果： F:\SEG\myResearch\myProject_2 注：argv[0]只是得到的是当前脚本的绝对位置；而os模块中的几种获得路径的方法，得到的是当前的工作目录，如:open('1.txt','r')，则会在当前工作目录查找该文件。即大部分的文件操作都是相对于当前工作路径。
如os.chdir(E:\Program Files),则大部分的文件操作就会是相对于E:\dir1。 fobj = open('Hello.txt')，实际会打开E:\Program Files\Hello.txt文件。 |