pandas基础 import pandas as pdPandas有两个最主要也是最重要的数据结构： Series 和 DataFrameSeriesSeries是一种类似于一维数组的 对象，由一组数据（各种NumPy数据类型）以及一组与之对应的索引（数据标签）组成。类似一维数组的对象由数据和索引组成索引(index)在左，数据(values)在右索引是自动创建的1. 通过list构建Seriesser_obj = pd.Series(range(10))示例代码：# 通过list构建Series ser_obj = pd.Series(range(10, 20)) print(ser_obj.head(3)) print(ser_obj) print(type(ser_obj))运行结果：0 10 1 11 2 12 dtype: int64 0 10 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 dtype: int64 <class 'pandas.core.series.Series'>2. 获取数据和索引ser_obj.index 和 ser_obj.values示例代码：# 获取数据 print(ser_obj.values) # 获取索引 print(ser_obj.index)运行结果：[10 11 12 13 14 15 16 17 18 19] RangeIndex(start=0, stop=10, step=1)3. 通过索引获取数据ser_obj[idx]示例代码：#通过索引获取数据 print(ser_obj[0]) print(ser_obj[8])运行结果：10 184. 索引与数据的对应关系不被运算结果影响示例代码：# 索引与数据的对应关系不被运算结果影响 print(ser_obj * 2) print(ser_obj > 15)运行结果：0 20 1 22 2 24 3 26 4 28 5 30 6 32 7 34 8 36 9 38 dtype: int64 0 False 1 False 2 False 3 False 4 False 5 False 6 True 7 True 8 True 9 True dtype: bool5. 通过dict构建Series示例代码：# 通过dict构建Series year_data = {2001: 17.8, 2002: 20.1, 2003: 16.5} ser_obj2 = pd.Series(year_data) print(ser_obj2.head()) print(ser_obj2.index)运行结果：2001 17.8 2002 20.1 2003 16.5 dtype: float64 Int64Index([2001, 2002, 2003], dtype='int64')name属性对象名：ser_obj.name对象索引名：ser_obj.index.name示例代码：# name属性 ser_obj2.name = 'temp' ser_obj2.index.name = 'year' print(ser_obj2.head())运行结果：year 2001 17.8 2002 20.1 2003 16.5 Name: temp, dtype: float64DataFrameDataFrame是一个表格型的数据结构，它含有一组有序的列，每列可以是不同类型的值。DataFrame既有行索引也有列索引，它可以被看做是由Series组成的字典（共用同一个索引），数据是以二维结构存放的。类似多维数组/表格数据 (如，excel, R中的data.frame)每列数据可以是不同的类型索引包括列索引和行索引1. 通过ndarray构建DataFrame示例代码：import numpy as np # 通过ndarray构建DataFrame array = np.random.randn(5,4) print(array) df_obj = pd.DataFrame(array) print(df_obj.head())运行结果：[[ 0.83500594 -1.49290138 -0.53120106 -0.11313932] [ 0.64629762 -0.36779941 0.08011084 0.60080495] [-1.23458522 0.33409674 -0.58778195 -0.73610573] [-1.47651414 0.99400187 0.21001995 -0.90515656] [ 0.56669419 1.38238348 -0.49099007 1.94484598]] 0 1 2 3 0 0.835006 -1.492901 -0.531201 -0.113139 1 0.646298 -0.367799 0.080111 0.600805 2 -1.234585 0.334097 -0.587782 -0.736106 3 -1.476514 0.994002 0.210020 -0.905157 4 0.566694 1.382383 -0.490990 1.9448462. 通过dict构建DataFrame示例代码：# 通过dict构建DataFrame dict_data = {'A': 1, 'B': pd.Timestamp('20170426'), 'C': pd.Series(1, index=list(range(4)),dtype='float32'), 'D': np.array([3] * 4,dtype='int32'), 'E': ["Python","Java","C++","C"], 'F': 'ITCast' } #print dict_data df_obj2 = pd.DataFrame(dict_data) print(df_obj2)运行结果： A B C D E F 0 1 2017-04-26 1.0 3 Python ITCast 1 1 2017-04-26 1.0 3 Java ITCast 2 1 2017-04-26 1.0 3 C++ ITCast 3 1 2017-04-26 1.0 3 C ITCast3. 通过列索引获取列数据（Series类型）df_obj[col_idx] 或 df_obj.col_idx示例代码：# 通过列索引获取列数据 print(df_obj2['A']) print(type(df_obj2['A'])) print(df_obj2.A)运行结果：0 1.0 1 1.0 2 1.0 3 1.0 Name: A, dtype: float64 <class 'pandas.core.series.Series'> 0 1.0 1 1.0 2 1.0 3 1.0 Name: A, dtype: float644. 增加列数据df_obj[new_col_idx] = data类似Python的 dict添加key-value示例代码：# 增加列 df_obj2['G'] = df_obj2['D'] + 4 print(df_obj2.head())运行结果： A B C D E F G 0 1.0 2017-01-02 1.0 3 Python ITCast 7 1 1.0 2017-01-02 1.0 3 Java ITCast 7 2 1.0 2017-01-02 1.0 3 C++ ITCast 7 3 1.0 2017-01-02 1.0 3 C ITCast 75. 删除列del df_obj[col_idx]示例代码：# 删除列 del(df_obj2['G'] ) print(df_obj2.head())运行结果： A B C D E F 0 1.0 2017-01-02 1.0 3 Python ITCast 1 1.0 2017-01-02 1.0 3 Java ITCast 2 1.0 2017-01-02 1.0 3 C++ ITCast 3 1.0 2017-01-02 1.0 3 C ITCastPandas的索引操作索引对象Index1. Series和DataFrame中的索引都是Index对象示例代码：print(type(ser_obj.index)) print(type(df_obj2.index)) print(df_obj2.index)运行结果：<class 'pandas.indexes.range.RangeIndex'> <class 'pandas.indexes.numeric.Int64Index'> Int64Index([0, 1, 2, 3], dtype='int64')2. 索引对象不可变，保证了数据的安全示例代码：# 索引对象不可变 df_obj2.index[0] = 2运行结果：--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-23-7f40a356d7d1> in <module>() 1 # 索引对象不可变 ----> 2 df_obj2.index[0] = 2 /Users/Power/anaconda/lib/python3.6/site-packages/pandas/indexes/base.py in __setitem__(self, key, value) 1402 1403 def __setitem__(self, key, value): -> 1404 raise TypeError("Index does not support mutable operations") 1405 1406 def __getitem__(self, key): TypeError: Index does not support mutable operations常见的Index种类Index，索引Int64Index，整数索引MultiIndex，层级索引DatetimeIndex，时间戳类型Series索引1. index 指定行索引名示例代码：ser_obj = pd.Series(range(5), index = ['a', 'b', 'c', 'd', 'e']) print(ser_obj.head())运行结果：a 0 b 1 c 2 d 3 e 4 dtype: int642. 行索引ser_obj[‘label’], ser_obj[pos]示例代码：# 行索引 print(ser_obj['b']) print(ser_obj[2])运行结果：1 23. 切片索引ser_obj[2:4], ser_obj[‘label1’: ’label3’]注意，按索引名切片操作时，是包含终止索引的。示例代码：# 切片索引 print(ser_obj[1:3]) print(ser_obj['b':'d'])运行结果：b 1 c 2 dtype: int64 b 1 c 2 d 3 dtype: int644. 不连续索引ser_obj[[‘label1’, ’label2’, ‘label3’]]示例代码：# 不连续索引 print(ser_obj[[0, 2, 4]]) print(ser_obj[['a', 'e']])运行结果：a 0 c 2 e 4 dtype: int64 a 0 e 4 dtype: int645. 布尔索引示例代码：# 布尔索引 ser_bool = ser_obj > 2 print(ser_bool) print(ser_obj[ser_bool]) print(ser_obj[ser_obj > 2])运行结果：a False b False c False d True e True dtype: bool d 3 e 4 dtype: int64 d 3 e 4 dtype: int64DataFrame索引1. columns 指定列索引名示例代码：import numpy as np df_obj = pd.DataFrame(np.random.randn(5,4), columns = ['a', 'b', 'c', 'd']) print(df_obj.head())运行结果： a b c d 0 -0.241678 0.621589 0.843546 -0.383105 1 -0.526918 -0.485325 1.124420 -0.653144 2 -1.074163 0.939324 -0.309822 -0.209149 3 -0.716816 1.844654 -2.123637 -1.323484 4 0.368212 -0.910324 0.064703 0.4860162. 列索引df_obj[[‘label’]]示例代码：# 列索引 print(df_obj['a']) # 返回Series类型 print(df_obj[[0]]) # 返回DataFrame类型 print(type(df_obj[[0]])) # 返回DataFrame类型运行结果：0 -0.241678 1 -0.526918 2 -1.074163 3 -0.716816 4 0.368212 Name: a, dtype: float64 <class 'pandas.core.frame.DataFrame'>3. 不连续索引df_obj[[‘label1’, ‘label2’]]示例代码：# 不连续索引 print(df_obj[['a','c']]) print(df_obj[[1, 3]])运行结果： a c 0 -0.241678 0.843546 1 -0.526918 1.124420 2 -1.074163 -0.309822 3 -0.716816 -2.123637 4 0.368212 0.064703 b d 0 0.621589 -0.383105 1 -0.485325 -0.653144 2 0.939324 -0.209149 3 1.844654 -1.323484 4 -0.910324 0.486016高级索引：标签、位置和混合Pandas的高级索引有3种1. loc 标签索引DataFrame 不能直接切片，可以通过loc来做切片loc是基于标签名的索引，也就是我们自定义的索引名示例代码：# 标签索引 loc # Series print(ser_obj['b':'d']) print(ser_obj.loc['b':'d']) # DataFrame print(df_obj['a']) # 第一个参数索引行，第二个参数是列 print(df_obj.loc[0:2, 'a'])运行结果：b 1 c 2 d 3 dtype: int64 b 1 c 2 d 3 dtype: int64 0 -0.241678 1 -0.526918 2 -1.074163 3 -0.716816 4 0.368212 Name: a, dtype: float64 0 -0.241678 1 -0.526918 2 -1.074163 Name: a, dtype: float642. iloc 位置索引作用和loc一样，不过是基于索引编号来索引示例代码：# 整型位置索引 iloc # Series print(ser_obj[1:3]) print(ser_obj.iloc[1:3]) # DataFrame print(df_obj.iloc[0:2, 0]) # 注意和df_obj.loc[0:2, 'a']的区别运行结果：b 1 c 2 dtype: int64 b 1 c 2 dtype: int64 0 -0.241678 1 -0.526918 Name: a, dtype: float643. ix 标签与位置混合索引ix是以上二者的综合，既可以使用索引编号，又可以使用自定义索引，要视情况不同来使用，如果索引既有数字又有英文，那么这种方式是不建议使用的，容易导致定位的混乱。示例代码：# 混合索引 ix # Series print(ser_obj.ix[1:3]) print(ser_obj.ix['b':'c']) # DataFrame print(df_obj.loc[0:2, 'a']) print(df_obj.ix[0:2, 0])运行结果：b 1 c 2 dtype: int64 b 1 c 2 dtype: int64 0 -0.241678 1 -0.526918 2 -1.074163 Name: a, dtype: float64注意DataFrame索引操作，可将其看作ndarray的索引操作标签的切片索引是包含末尾位置的Pandas的对齐运算是数据清洗的重要过程，可以按索引对齐进行运算，如果没对齐的位置则补NaN，最后也可以填充NaNSeries的对齐运算1. Series 按行、索引对齐示例代码：s1 = pd.Series(range(10, 20), index = range(10)) s2 = pd.Series(range(20, 25), index = range(5)) print('s1: ' ) print(s1) print('') print('s2: ') print(s2)运行结果：s1: 0 10 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 dtype: int64 s2: 0 20 1 21 2 22 3 23 4 24 dtype: int642. Series的对齐运算示例代码：# Series 对齐运算 s1 + s2运行结果：0 30.0 1 32.0 2 34.0 3 36.0 4 38.0 5 NaN 6 NaN 7 NaN 8 NaN 9 NaN dtype: float64DataFrame的对齐运算1. DataFrame按行、列索引对齐示例代码：df1 = pd.DataFrame(np.ones((2,2)), columns = ['a', 'b']) df2 = pd.DataFrame(np.ones((3,3)), columns = ['a', 'b', 'c']) print('df1: ') print(df1) print('') print('df2: ') print(df2)运行结果：df1: a b 0 1.0 1.0 1 1.0 1.0 df2: a b c 0 1.0 1.0 1.0 1 1.0 1.0 1.0 2 1.0 1.0 1.02. DataFrame的对齐运算示例代码：# DataFrame对齐操作 df1 + df2运行结果： a b c 0 2.0 2.0 NaN 1 2.0 2.0 NaN 2 NaN NaN NaN填充未对齐的数据进行运算1. fill_value使用add, sub, div, mul的同时，通过fill_value指定填充值，未对齐的数据将和填充值做运算示例代码：print(s1) print(s2) s1.add(s2, fill_value = -1) print(df1) print(df2) df1.sub(df2, fill_value = 2.)运行结果：# print(s1) 0 10 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 dtype: int64 # print(s2) 0 20 1 21 2 22 3 23 4 24 dtype: int64 # s1.add(s2, fill_value = -1) 0 30.0 1 32.0 2 34.0 3 36.0 4 38.0 5 14.0 6 15.0 7 16.0 8 17.0 9 18.0 dtype: float64 # print(df1) a b 0 1.0 1.0 1 1.0 1.0 # print(df2) a b c 0 1.0 1.0 1.0 1 1.0 1.0 1.0 2 1.0 1.0 1.0 # df1.sub(df2, fill_value = 2.) a b c 0 0.0 0.0 1.0 1 0.0 0.0 1.0 2 1.0 1.0 1.0Pandas的函数应用apply 和 applymap1. 可直接使用NumPy的函数示例代码：# Numpy ufunc 函数 df = pd.DataFrame(np.random.randn(5,4) - 1) print(df) print(np.abs(df))运行结果： 0 1 2 3 0 -0.062413 0.844813 -1.853721 -1.980717 1 -0.539628 -1.975173 -0.856597 -2.612406 2 -1.277081 -1.088457 -0.152189 0.530325 3 -1.356578 -1.996441 0.368822 -2.211478 4 -0.562777 0.518648 -2.007223 0.059411 0 1 2 3 0 0.062413 0.844813 1.853721 1.980717 1 0.539628 1.975173 0.856597 2.612406 2 1.277081 1.088457 0.152189 0.530325 3 1.356578 1.996441 0.368822 2.211478 4 0.562777 0.518648 2.007223 0.0594112. 通过apply将函数应用到列或行上示例代码：# 使用apply应用行或列数据 #f = lambda x : x.max() print(df.apply(lambda x : x.max()))运行结果：0 -0.062413 1 0.844813 2 0.368822 3 0.530325 dtype: float64注意指定轴的方向，默认axis=0，方向是列示例代码：# 指定轴方向，axis=1，方向是行 print(df.apply(lambda x : x.max(), axis=1))运行结果：0 0.844813 1 -0.539628 2 0.530325 3 0.368822 4 0.518648 dtype: float643. 通过applymap将函数应用到每个数据上示例代码：# 使用applymap应用到每个数据 f2 = lambda x : '%.2f' % x print(df.applymap(f2))运行结果： 0 1 2 3 0 -0.06 0.84 -1.85 -1.98 1 -0.54 -1.98 -0.86 -2.61 2 -1.28 -1.09 -0.15 0.53 3 -1.36 -2.00 0.37 -2.21 4 -0.56 0.52 -2.01 0.06排序1. 索引排序sort_index()排序默认使用升序排序，ascending=False 为降序排序示例代码：# Series s4 = pd.Series(range(10, 15), index = np.random.randint(5, size=5)) print(s4) # 索引排序 s4.sort_index() # 0 0 1 3 3运行结果：0 10 3 11 1 12 3 13 0 14 dtype: int64 0 10 0 14 1 12 3 11 3 13 dtype: int64对DataFrame操作时注意轴方向示例代码：# DataFrame df4 = pd.DataFrame(np.random.randn(3, 5), index=np.random.randint(3, size=3), columns=np.random.randint(5, size=5)) print(df4) df4_isort = df4.sort_index(axis=1, ascending=False) print(df4_isort) # 4 2 1 1 0运行结果： 1 4 0 1 2 2 -0.416686 -0.161256 0.088802 -0.004294 1.164138 1 -0.671914 0.531256 0.303222 -0.509493 -0.342573 1 1.988321 -0.466987 2.787891 -1.105912 0.889082 4 2 1 1 0 2 -0.161256 1.164138 -0.416686 -0.004294 0.088802 1 0.531256 -0.342573 -0.671914 -0.509493 0.303222 1 -0.466987 0.889082 1.988321 -1.105912 2.7878912. 按值排序sort_values(by='column name')根据某个唯一的列名进行排序，如果有其他相同列名则报错。示例代码：# 按值排序 df4_vsort = df4.sort_values(by=0, ascending=False) print(df4_vsort)运行结果： 1 4 0 1 2 1 1.988321 -0.466987 2.787891 -1.105912 0.889082 1 -0.671914 0.531256 0.303222 -0.509493 -0.342573 2 -0.416686 -0.161256 0.088802 -0.004294 1.164138处理缺失数据示例代码：df_data = pd.DataFrame([np.random.randn(3), [1., 2., np.nan], [np.nan, 4., np.nan], [1., 2., 3.]]) print(df_data.head())运行结果： 0 1 2 0 -0.281885 -0.786572 0.487126 1 1.000000 2.000000 NaN 2 NaN 4.000000 NaN 3 1.000000 2.000000 3.0000001. 判断是否存在缺失值：isnull()示例代码：# isnull print(df_data.isnull())运行结果： 0 1 2 0 False False False 1 False False True 2 True False True 3 False False False2. 丢弃缺失数据：dropna()根据axis轴方向，丢弃包含NaN的行或列。 示例代码：# dropna print(df_data.dropna()) print(df_data.dropna(axis=1))运行结果： 0 1 2 0 -0.281885 -0.786572 0.487126 3 1.000000 2.000000 3.000000 1 0 -0.786572 1 2.000000 2 4.000000 3 2.0000003. 填充缺失数据：fillna()示例代码：# fillna print(df_data.fillna(-100.))运行结果： 0 1 2 0 -0.281885 -0.786572 0.487126 1 1.000000 2.000000 -100.000000 2 -100.000000 4.000000 -100.000000 3 1.000000 2.000000 3.000000层级索引（hierarchical indexing）下面创建一个Series， 在输入索引Index时，输入了由两个子list组成的list，第一个子list是外层索引，第二个list是内层索引。示例代码：import pandas as pd import numpy as np ser_obj = pd.Series(np.random.randn(12),index=[ ['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd'], [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2] ]) print(ser_obj)运行结果：a 0 0.099174 1 -0.310414 2 -0.558047 b 0 1.742445 1 1.152924 2 -0.725332 c 0 -0.150638 1 0.251660 2 0.063387 d 0 1.080605 1 0.567547 2 -0.154148 dtype: float64MultiIndex索引对象打印这个Series的索引类型，显示是MultiIndex直接将索引打印出来，可以看到有lavels,和labels两个信息。lavels表示两个层级中分别有那些标签，labels是每个位置分别是什么标签。示例代码：print(type(ser_obj.index)) print(ser_obj.index)运行结果：<class 'pandas.indexes.multi.MultiIndex'> MultiIndex(levels=[['a', 'b', 'c', 'd'], [0, 1, 2]], labels=[[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]])选取子集根据索引获取数据。因为现在有两层索引，当通过外层索引获取数据的时候，可以直接利用外层索引的标签来获取。当要通过内层索引获取数据的时候，在list中传入两个元素，前者是表示要选取的外层索引，后者表示要选取的内层索引。1. 外层选取：ser_obj['outer_label']示例代码：# 外层选取 print(ser_obj['c'])运行结果：0 -1.362096 1 1.558091 2 -0.452313 dtype: float642. 内层选取：ser_obj[:, 'inner_label']示例代码：# 内层选取 print(ser_obj[:, 2])运行结果：a 0.826662 b 0.015426 c -0.452313 d -0.051063 dtype: float64常用于分组操作、透视表的生成等交换分层顺序1. swaplevel().swaplevel( )交换内层与外层索引。示例代码：print(ser_obj.swaplevel())运行结果：0 a 0.099174 1 a -0.310414 2 a -0.558047 0 b 1.742445 1 b 1.152924 2 b -0.725332 0 c -0.150638 1 c 0.251660 2 c 0.063387 0 d 1.080605 1 d 0.567547 2 d -0.154148 dtype: float64交换并排序分层sortlevel().sortlevel( )先对外层索引进行排序，再对内层索引进行排序，默认是升序。示例代码：# 交换并排序分层 print(ser_obj.swaplevel().sortlevel())运行结果：0 a 0.099174 b 1.742445 c -0.150638 d 1.080605 1 a -0.310414 b 1.152924 c 0.251660 d 0.567547 2 a -0.558047 b -0.725332 c 0.063387 d -0.154148 dtype: float64Pandas统计计算和描述示例代码：import numpy as np import pandas as pd df_obj = pd.DataFrame(np.random.randn(5,4), columns = ['a', 'b', 'c', 'd']) print(df_obj)运行结果： a b c d 0 1.469682 1.948965 1.373124 -0.564129 1 -1.466670 -0.494591 0.467787 -2.007771 2 1.368750 0.532142 0.487862 -1.130825 3 -0.758540 -0.479684 1.239135 1.073077 4 -0.007470 0.997034 2.669219 0.742070常用的统计计算sum, mean, max, min…axis=0 按列统计，axis=1按行统计skipna 排除缺失值， 默认为True示例代码：df_obj.sum() df_obj.max() df_obj.min(axis=1, skipna=False)运行结果：a 0.605751 b 2.503866 c 6.237127 d -1.887578 dtype: float64 a 1.469682 b 1.948965 c 2.669219 d 1.073077 dtype: float64 0 -0.564129 1 -2.007771 2 -1.130825 3 -0.758540 4 -0.007470 dtype: float64常用的统计描述describe 产生多个统计数据示例代码：print(df_obj.describe())运行结果： a b c d count 5.000000 5.000000 5.000000 5.000000 mean 0.180305 0.106488 0.244978 0.178046 std 0.641945 0.454340 1.064356 1.144416 min -0.677175 -0.490278 -1.164928 -1.574556 25% -0.064069 -0.182920 -0.464013 -0.089962 50% 0.231722 0.127846 0.355859 0.190482 75% 0.318854 0.463377 1.169750 0.983663 max 1.092195 0.614413 1.328220 1.380601常用的统计描述方法： Pandas分组与聚合分组 (groupby)对数据集进行分组，然后对每组进行统计分析SQL能够对数据进行过滤，分组聚合pandas能利用groupby进行更加复杂的分组运算分组运算过程：split->apply->combine拆分：进行分组的根据应用：每个分组运行的计算规则合并：把每个分组的计算结果合并起来示例代码：import pandas as pd import numpy as np dict_obj = {'key1' : ['a', 'b', 'a', 'b', 'a', 'b', 'a', 'a'], 'key2' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], 'data1': np.random.randn(8), 'data2': np.random.randn(8)} df_obj = pd.DataFrame(dict_obj) print(df_obj)运行结果： data1 data2 key1 key2 0 0.974685 -0.672494 a one 1 -0.214324 0.758372 b one 2 1.508838 0.392787 a two 3 0.522911 0.630814 b three 4 1.347359 -0.177858 a two 5 -0.264616 1.017155 b two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three一、GroupBy对象：DataFrameGroupBy，SeriesGroupBy1. 分组操作groupby()进行分组，GroupBy对象没有进行实际运算，只是包含分组的中间数据按列名分组：obj.groupby(‘label’)示例代码：# dataframe根据key1进行分组 print(type(df_obj.groupby('key1'))) # dataframe的 data1 列根据 key1 进行分组 print(type(df_obj['data1'].groupby(df_obj['key1'])))运行结果：<class 'pandas.core.groupby.DataFrameGroupBy'> <class 'pandas.core.groupby.SeriesGroupBy'>2. 分组运算对GroupBy对象进行分组运算/多重分组运算，如mean()非数值数据不进行分组运算示例代码：# 分组运算 grouped1 = df_obj.groupby('key1') print(grouped1.mean()) grouped2 = df_obj['data1'].groupby(df_obj['key1']) print(grouped2.mean())运行结果： data1 data2 key1 a 0.437389 -0.230101 b 0.014657 0.802114 key1 a 0.437389 b 0.014657 Name: data1, dtype: float64size() 返回每个分组的元素个数示例代码：# size print(grouped1.size()) print(grouped2.size())运行结果：key1 a 5 b 3 dtype: int64 key1 a 5 b 3 dtype: int643. 按自定义的key分组obj.groupby(self_def_key)自定义的key可为列表或多层列表obj.groupby([‘label1’, ‘label2’])->多层dataframe示例代码：# 按自定义key分组，列表 self_def_key = [0, 1, 2, 3, 3, 4, 5, 7] print(df_obj.groupby(self_def_key).size()) # 按自定义key分组，多层列表 print(df_obj.groupby([df_obj['key1'], df_obj['key2']]).size()) # 按多个列多层分组 grouped2 = df_obj.groupby(['key1', 'key2']) print(grouped2.size()) # 多层分组按key的顺序进行 grouped3 = df_obj.groupby(['key2', 'key1']) print(grouped3.mean()) # unstack可以将多层索引的结果转换成单层的dataframe print(grouped3.mean().unstack())运行结果：0 1 1 1 2 1 3 2 4 1 5 1 7 1 dtype: int64 key1 key2 a one 2 three 1 two 2 b one 1 three 1 two 1 dtype: int64 key1 key2 a one 2 three 1 two 2 b one 1 three 1 two 1 dtype: int64 data1 data2 key2 key1 one a 0.174988 -0.110804 b -0.214324 0.758372 three a -1.019229 -1.143825 b 0.522911 0.630814 two a 1.428099 0.107465 b -0.264616 1.017155 data1 data2 key1 a b a b key2 one 0.174988 -0.214324 -0.110804 0.758372 three -1.019229 0.522911 -1.143825 0.630814 two 1.428099 -0.264616 0.107465 1.017155二、GroupBy对象支持迭代操作每次迭代返回一个元组 (group_name, group_data)可用于分组数据的具体运算1. 单层分组示例代码：# 单层分组，根据key1 for group_name, group_data in grouped1: print(group_name) print(group_data)运行结果：a data1 data2 key1 key2 0 0.974685 -0.672494 a one 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three b data1 data2 key1 key2 1 -0.214324 0.758372 b one 3 0.522911 0.630814 b three 5 -0.264616 1.017155 b two2. 多层分组示例代码：# 多层分组，根据key1 和 key2 for group_name, group_data in grouped2: print(group_name) print(group_data)运行结果：('a', 'one') data1 data2 key1 key2 0 0.974685 -0.672494 a one 6 -0.624708 0.450885 a one ('a', 'three') data1 data2 key1 key2 7 -1.019229 -1.143825 a three ('a', 'two') data1 data2 key1 key2 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two ('b', 'one') data1 data2 key1 key2 1 -0.214324 0.758372 b one ('b', 'three') data1 data2 key1 key2 3 0.522911 0.630814 b three ('b', 'two') data1 data2 key1 key2 5 -0.264616 1.017155 b two三、GroupBy对象可以转换成列表或字典示例代码：# GroupBy对象转换list print(list(grouped1)) # GroupBy对象转换dict print(dict(list(grouped1)))运行结果：[('a', data1 data2 key1 key2 0 0.974685 -0.672494 a one 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three), ('b', data1 data2 key1 key2 1 -0.214324 0.758372 b one 3 0.522911 0.630814 b three 5 -0.264616 1.017155 b two)] {'a': data1 data2 key1 key2 0 0.974685 -0.672494 a one 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three, 'b': data1 data2 key1 key2 1 -0.214324 0.758372 b one 3 0.522911 0.630814 b three 5 -0.264616 1.017155 b two}1. 按列分组、按数据类型分组示例代码：# 按列分组 print(df_obj.dtypes) # 按数据类型分组 print(df_obj.groupby(df_obj.dtypes, axis=1).size()) print(df_obj.groupby(df_obj.dtypes, axis=1).sum())运行结果：data1 float64 data2 float64 key1 object key2 object dtype: object float64 2 object 2 dtype: int64 float64 object 0 0.302191 a one 1 0.544048 b one 2 1.901626 a two 3 1.153725 b three 4 1.169501 a two 5 0.752539 b two 6 -0.173823 a one 7 -2.163054 a three2. 其他分组方法示例代码：df_obj2 = pd.DataFrame(np.random.randint(1, 10, (5,5)), columns=['a', 'b', 'c', 'd', 'e'], index=['A', 'B', 'C', 'D', 'E']) df_obj2.ix[1, 1:4] = np.NaN print(df_obj2)运行结果： a b c d e A 7 2.0 4.0 5.0 8 B 4 NaN NaN NaN 1 C 3 2.0 5.0 4.0 6 D 3 1.0 9.0 7.0 3 E 6 1.0 6.0 8.0 13. 通过字典分组示例代码：# 通过字典分组 mapping_dict = {'a':'Python', 'b':'Python', 'c':'Java', 'd':'C', 'e':'Java'} print(df_obj2.groupby(mapping_dict, axis=1).size()) print(df_obj2.groupby(mapping_dict, axis=1).count()) # 非NaN的个数 print(df_obj2.groupby(mapping_dict, axis=1).sum())运行结果：C 1 Java 2 Python 2 dtype: int64 C Java Python A 1 2 2 B 0 1 1 C 1 2 2 D 1 2 2 E 1 2 2 C Java Python A 5.0 12.0 9.0 B NaN 1.0 4.0 C 4.0 11.0 5.0 D 7.0 12.0 4.0 E 8.0 7.0 7.04. 通过函数分组，函数传入的参数为行索引或列索引示例代码：# 通过函数分组 df_obj3 = pd.DataFrame(np.random.randint(1, 10, (5,5)), columns=['a', 'b', 'c', 'd', 'e'], index=['AA', 'BBB', 'CC', 'D', 'EE']) #df_obj3 def group_key(idx): """ idx 为列索引或行索引 """ #return idx return len(idx) print(df_obj3.groupby(group_key).size()) # 以上自定义函数等价于 #df_obj3.groupby(len).size()运行结果：1 1 2 3 3 1 dtype: int645. 通过索引级别分组示例代码：# 通过索引级别分组 columns = pd.MultiIndex.from_arrays([['Python', 'Java', 'Python', 'Java', 'Python'], ['A', 'A', 'B', 'C', 'B']], names=['language', 'index']) df_obj4 = pd.DataFrame(np.random.randint(1, 10, (5, 5)), columns=columns) print(df_obj4) # 根据language进行分组 print(df_obj4.groupby(level='language', axis=1).sum()) # 根据index进行分组 print(df_obj4.groupby(level='index', axis=1).sum())运行结果：language Python Java Python Java Python index A A B C B 0 2 7 8 4 3 1 5 2 6 1 2 2 6 4 4 5 2 3 4 7 4 3 1 4 7 4 3 4 8 language Java Python 0 11 13 1 3 13 2 9 12 3 10 9 4 8 18 index A B C 0 9 11 4 1 7 8 1 2 10 6 5 3 11 5 3 4 11 11 4聚合 (aggregation)数组产生标量的过程，如mean()、count()等常用于对分组后的数据进行计算示例代码：dict_obj = {'key1' : ['a', 'b', 'a', 'b', 'a', 'b', 'a', 'a'], 'key2' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], 'data1': np.random.randint(1,10, 8), 'data2': np.random.randint(1,10, 8)} df_obj5 = pd.DataFrame(dict_obj) print(df_obj5)运行结果： data1 data2 key1 key2 0 3 7 a one 1 1 5 b one 2 7 4 a two 3 2 4 b three 4 6 4 a two 5 9 9 b two 6 3 5 a one 7 8 4 a three1. 内置的聚合函数sum(), mean(), max(), min(), count(), size(), describe()示例代码：print(df_obj5.groupby('key1').sum()) print(df_obj5.groupby('key1').max()) print(df_obj5.groupby('key1').min()) print(df_obj5.groupby('key1').mean()) print(df_obj5.groupby('key1').size()) print(df_obj5.groupby('key1').count()) print(df_obj5.groupby('key1').describe())运行结果： data1 data2 key1 a 27 24 b 12 18 data1 data2 key2 key1 a 8 7 two b 9 9 two data1 data2 key2 key1 a 3 4 one b 1 4 one data1 data2 key1 a 5.4 4.8 b 4.0 6.0 key1 a 5 b 3 dtype: int64 data1 data2 key2 key1 a 5 5 5 b 3 3 3 data1 data2 key1 a count 5.000000 5.000000 mean 5.400000 4.800000 std 2.302173 1.303840 min 3.000000 4.000000 25% 3.000000 4.000000 50% 6.000000 4.000000 75% 7.000000 5.000000 max 8.000000 7.000000 b count 3.000000 3.000000 mean 4.000000 6.000000 std 4.358899 2.645751 min 1.000000 4.000000 25% 1.500000 4.500000 50% 2.000000 5.000000 75% 5.500000 7.000000 max 9.000000 9.0000002. 可自定义函数，传入agg方法中grouped.agg(func)func的参数为groupby索引对应的记录示例代码：# 自定义聚合函数 def peak_range(df): """ 返回数值范围 """ #print type(df) #参数为索引所对应的记录 return df.max() - df.min() print(df_obj5.groupby('key1').agg(peak_range)) print(df_obj.groupby('key1').agg(lambda df : df.max() - df.min()))运行结果： data1 data2 key1 a 5 3 b 8 5 data1 data2 key1 a 2.528067 1.594711 b 0.787527 0.386341 In [25]:3. 应用多个聚合函数同时应用多个函数进行聚合操作，使用函数列表示例代码：# 应用多个聚合函数 # 同时应用多个聚合函数 print(df_obj.groupby('key1').agg(['mean', 'std', 'count', peak_range])) # 默认列名为函数名 print(df_obj.groupby('key1').agg(['mean', 'std', 'count', ('range', peak_range)])) # 通过元组提供新的列名运行结果： data1 data2 mean std count peak_range mean std count peak_range key1 a 0.437389 1.174151 5 2.528067 -0.230101 0.686488 5 1.594711 b 0.014657 0.440878 3 0.787527 0.802114 0.196850 3 0.386341 data1 data2 mean std count range mean std count range key1 a 0.437389 1.174151 5 2.528067 -0.230101 0.686488 5 1.594711 b 0.014657 0.440878 3 0.787527 0.802114 0.196850 3 0.3863414. 对不同的列分别作用不同的聚合函数，使用dict示例代码：# 每列作用不同的聚合函数 dict_mapping = {'data1':'mean', 'data2':'sum'} print(df_obj.groupby('key1').agg(dict_mapping)) dict_mapping = {'data1':['mean','max'], 'data2':'sum'} print(df_obj.groupby('key1').agg(dict_mapping))运行结果： data1 data2 key1 a 0.437389 -1.150505 b 0.014657 2.406341 data1 data2 mean max sum key1 a 0.437389 1.508838 -1.150505 b 0.014657 0.522911 2.4063415. 常用的内置聚合函数数据的分组运算示例代码：import pandas as pd import numpy as np dict_obj = {'key1' : ['a', 'b', 'a', 'b', 'a', 'b', 'a', 'a'], 'key2' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'], 'data1': np.random.randint(1, 10, 8), 'data2': np.random.randint(1, 10, 8)} df_obj = pd.DataFrame(dict_obj) print(df_obj) # 按key1分组后，计算data1，data2的统计信息并附加到原始表格中，并添加表头前缀 k1_sum = df_obj.groupby('key1').sum().add_prefix('sum_') print(k1_sum)运行结果： data1 data2 key1 key2 0 5 1 a one 1 7 8 b one 2 1 9 a two 3 2 6 b three 4 9 8 a two 5 8 3 b two 6 3 5 a one 7 8 3 a three sum_data1 sum_data2 key1 a 26 26 b 17 17聚合运算后会改变原始数据的形状，如何保持原始数据的形状?1. merge使用merge的外连接，比较复杂示例代码：# 方法1，使用merge k1_sum_merge = pd.merge(df_obj, k1_sum, left_on='key1', right_index=True) print(k1_sum_merge)运行结果： data1 data2 key1 key2 sum_data1 sum_data2 0 5 1 a one 26 26 2 1 9 a two 26 26 4 9 8 a two 26 26 6 3 5 a one 26 26 7 8 3 a three 26 26 1 7 8 b one 17 17 3 2 6 b three 17 17 5 8 3 b two 17 172. transformtransform的计算结果和原始数据的形状保持一致，如：grouped.transform(np.sum)示例代码：# 方法2，使用transform k1_sum_tf = df_obj.groupby('key1').transform(np.sum).add_prefix('sum_') df_obj[k1_sum_tf.columns] = k1_sum_tf print(df_obj)运行结果： data1 data2 key1 key2 sum_data1 sum_data2 sum_key2 0 5 1 a one 26 26 onetwotwoonethree 1 7 8 b one 17 17 onethreetwo 2 1 9 a two 26 26 onetwotwoonethree 3 2 6 b three 17 17 onethreetwo 4 9 8 a two 26 26 onetwotwoonethree 5 8 3 b two 17 17 onethreetwo 6 3 5 a one 26 26 onetwotwoonethree 7 8 3 a three 26 26 onetwotwoonethree也可传入自定义函数，示例代码：# 自定义函数传入transform def diff_mean(s): """ 返回数据与均值的差值 """ return s - s.mean() print(df_obj.groupby('key1').transform(diff_mean))运行结果： data1 data2 sum_data1 sum_data2 0 -0.200000 -4.200000 0 0 1 1.333333 2.333333 0 0 2 -4.200000 3.800000 0 0 3 -3.666667 0.333333 0 0 4 3.800000 2.800000 0 0 5 2.333333 -2.666667 0 0 6 -2.200000 -0.200000 0 0 7 2.800000 -2.200000 0 0groupby.apply(func)func函数也可以在各分组上分别调用，最后结果通过pd.concat组装到一起（数据合并）示例代码：import pandas as pd import numpy as np dataset_path = './starcraft.csv' df_data = pd.read_csv(dataset_path, usecols=['LeagueIndex', 'Age', 'HoursPerWeek', 'TotalHours', 'APM']) def top_n(df, n=3, column='APM'): """ 返回每个分组按 column 的 top n 数据 """ return df.sort_values(by=column, ascending=False)[:n] print(df_data.groupby('LeagueIndex').apply(top_n))运行结果： LeagueIndex Age HoursPerWeek TotalHours APM LeagueIndex 1 2214 1 20.0 12.0 730.0 172.9530 2246 1 27.0 8.0 250.0 141.6282 1753 1 20.0 28.0 100.0 139.6362 2 3062 2 20.0 6.0 100.0 179.6250 3229 2 16.0 24.0 110.0 156.7380 1520 2 29.0 6.0 250.0 151.6470 3 1557 3 22.0 6.0 200.0 226.6554 484 3 19.0 42.0 450.0 220.0692 2883 3 16.0 8.0 800.0 208.9500 4 2688 4 26.0 24.0 990.0 249.0210 1759 4 16.0 6.0 75.0 229.9122 2637 4 23.0 24.0 650.0 227.2272 5 3277 5 18.0 16.0 950.0 372.6426 93 5 17.0 36.0 720.0 335.4990 202 5 37.0 14.0 800.0 327.7218 6 734 6 16.0 28.0 730.0 389.8314 2746 6 16.0 28.0 4000.0 350.4114 1810 6 21.0 14.0 730.0 323.2506 7 3127 7 23.0 42.0 2000.0 298.7952 104 7 21.0 24.0 1000.0 286.4538 1654 7 18.0 98.0 700.0 236.0316 8 3393 8 NaN NaN NaN 375.8664 3373 8 NaN NaN NaN 364.8504 3372 8 NaN NaN NaN 355.35181. 产生层级索引：外层索引是分组名，内层索引是df_obj的行索引示例代码：# apply函数接收的参数会传入自定义的函数中 print(df_data.groupby('LeagueIndex').apply(top_n, n=2, column='Age'))运行结果： LeagueIndex Age HoursPerWeek TotalHours APM LeagueIndex 1 3146 1 40.0 12.0 150.0 38.5590 3040 1 39.0 10.0 500.0 29.8764 2 920 2 43.0 10.0 730.0 86.0586 2437 2 41.0 4.0 200.0 54.2166 3 1258 3 41.0 14.0 800.0 77.6472 2972 3 40.0 10.0 500.0 60.5970 4 1696 4 44.0 6.0 500.0 89.5266 1729 4 39.0 8.0 500.0 86.7246 5 202 5 37.0 14.0 800.0 327.7218 2745 5 37.0 18.0 1000.0 123.4098 6 3069 6 31.0 8.0 800.0 133.1790 2706 6 31.0 8.0 700.0 66.9918 7 2813 7 26.0 36.0 1300.0 188.5512 1992 7 26.0 24.0 1000.0 219.6690 8 3340 8 NaN NaN NaN 189.7404 3341 8 NaN NaN NaN 287.81282. 禁止层级索引, group_keys=False示例代码：print(df_data.groupby('LeagueIndex', group_keys=False).apply(top_n))运行结果： LeagueIndex Age HoursPerWeek TotalHours APM 2214 1 20.0 12.0 730.0 172.9530 2246 1 27.0 8.0 250.0 141.6282 1753 1 20.0 28.0 100.0 139.6362 3062 2 20.0 6.0 100.0 179.6250 3229 2 16.0 24.0 110.0 156.7380 1520 2 29.0 6.0 250.0 151.6470 1557 3 22.0 6.0 200.0 226.6554 484 3 19.0 42.0 450.0 220.0692 2883 3 16.0 8.0 800.0 208.9500 2688 4 26.0 24.0 990.0 249.0210 1759 4 16.0 6.0 75.0 229.9122 2637 4 23.0 24.0 650.0 227.2272 3277 5 18.0 16.0 950.0 372.6426 93 5 17.0 36.0 720.0 335.4990 202 5 37.0 14.0 800.0 327.7218 734 6 16.0 28.0 730.0 389.8314 2746 6 16.0 28.0 4000.0 350.4114 1810 6 21.0 14.0 730.0 323.2506 3127 7 23.0 42.0 2000.0 298.7952 104 7 21.0 24.0 1000.0 286.4538 1654 7 18.0 98.0 700.0 236.0316 3393 8 NaN NaN NaN 375.8664 3373 8 NaN NaN NaN 364.8504 3372 8 NaN NaN NaN 355.3518apply可以用来处理不同分组内的缺失数据填充，填充该分组的均值。数据清洗数据清洗是数据分析关键的一步，直接影响之后的处理工作数据需要修改吗？有什么需要修改的吗？数据应该怎么调整才能适用于接下来的分析和挖掘？是一个迭代的过程，实际项目中可能需要不止一次地执行这些清洗操作处理缺失数据：pd.fillna()，pd.dropna()数据连接(pd.merge)pd.merge根据单个或多个键将不同DataFrame的行连接起来类似数据库的连接操作示例代码：import pandas as pd import numpy as np df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data1' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'key': ['a', 'b', 'd'], 'data2' : np.random.randint(0,10,3)}) print(df_obj1) print(df_obj2)运行结果： data1 key data1 key 0 8 b 1 8 b 2 3 a 3 5 c 4 4 a 5 9 a 6 6 b data2 key 0 9 a 1 0 b 2 3 d1. 默认将重叠列的列名作为“外键”进行连接示例代码：# 默认将重叠列的列名作为“外键”进行连接 print(pd.merge(df_obj1, df_obj2))运行结果： data1 key data2 0 8 b 0 1 8 b 0 2 6 b 0 3 3 a 9 4 4 a 9 5 9 a 92. on显示指定“外键”示例代码：# on显示指定“外键” print(pd.merge(df_obj1, df_obj2, on='key'))运行结果： data1 key data2 0 8 b 0 1 8 b 0 2 6 b 0 3 3 a 9 4 4 a 9 5 9 a 93. left_on，左侧数据的“外键”，right_on，右侧数据的“外键”示例代码：# left_on，right_on分别指定左侧数据和右侧数据的“外键” # 更改列名 df_obj1 = df_obj1.rename(columns={'key':'key1'}) df_obj2 = df_obj2.rename(columns={'key':'key2'}) print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2'))运行结果： data1 key1 data2 key2 0 8 b 0 b 1 8 b 0 b 2 6 b 0 b 3 3 a 9 a 4 4 a 9 a 5 9 a 9 a默认是“内连接”(inner)，即结果中的键是交集how指定连接方式4. “外连接”(outer)，结果中的键是并集示例代码：# “外连接” print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2', how='outer'))运行结果： data1 key1 data2 key2 0 8.0 b 0.0 b 1 8.0 b 0.0 b 2 6.0 b 0.0 b 3 3.0 a 9.0 a 4 4.0 a 9.0 a 5 9.0 a 9.0 a 6 5.0 c NaN NaN 7 NaN NaN 3.0 d5. “左连接”(left)示例代码：# 左连接 print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2', how='left'))运行结果： data1 key1 data2 key2 0 8 b 0.0 b 1 8 b 0.0 b 2 3 a 9.0 a 3 5 c NaN NaN 4 4 a 9.0 a 5 9 a 9.0 a 6 6 b 0.0 b6. “右连接”(right)示例代码：# 右连接 print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2', how='right'))运行结果： data1 key1 data2 key2 0 8.0 b 0 b 1 8.0 b 0 b 2 6.0 b 0 b 3 3.0 a 9 a 4 4.0 a 9 a 5 9.0 a 9 a 6 NaN NaN 3 d7. 处理重复列名suffixes，默认为_x, _y示例代码：# 处理重复列名 df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'key': ['a', 'b', 'd'], 'data' : np.random.randint(0,10,3)}) print(pd.merge(df_obj1, df_obj2, on='key', suffixes=('_left', '_right')))运行结果： data_left key data_right 0 9 b 1 1 5 b 1 2 1 b 1 3 2 a 8 4 2 a 8 5 5 a 88. 按索引连接left_index=True或right_index=True示例代码：# 按索引连接 df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data1' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'data2' : np.random.randint(0,10,3)}, index=['a', 'b', 'd']) print(pd.merge(df_obj1, df_obj2, left_on='key', right_index=True))运行结果： data1 key data2 0 3 b 6 1 4 b 6 6 8 b 6 2 6 a 0 4 3 a 0 5 0 a 0数据合并(pd.concat)沿轴方向将多个对象合并到一起1. NumPy的concatnp.concatenate示例代码：import numpy as np import pandas as pd arr1 = np.random.randint(0, 10, (3, 4)) arr2 = np.random.randint(0, 10, (3, 4)) print(arr1) print(arr2) print(np.concatenate([arr1, arr2])) print(np.concatenate([arr1, arr2], axis=1))运行结果：# print(arr1) [[3 3 0 8] [2 0 3 1] [4 8 8 2]] # print(arr2) [[6 8 7 3] [1 6 8 7] [1 4 7 1]] # print(np.concatenate([arr1, arr2])) [[3 3 0 8] [2 0 3 1] [4 8 8 2] [6 8 7 3] [1 6 8 7] [1 4 7 1]] # print(np.concatenate([arr1, arr2], axis=1)) [[3 3 0 8 6 8 7 3] [2 0 3 1 1 6 8 7] [4 8 8 2 1 4 7 1]]2. pd.concat注意指定轴方向，默认axis=0join指定合并方式，默认为outerSeries合并时查看行索引有无重复1) index 没有重复的情况示例代码：# index 没有重复的情况 ser_obj1 = pd.Series(np.random.randint(0, 10, 5), index=range(0,5)) ser_obj2 = pd.Series(np.random.randint(0, 10, 4), index=range(5,9)) ser_obj3 = pd.Series(np.random.randint(0, 10, 3), index=range(9,12)) print(ser_obj1) print(ser_obj2) print(ser_obj3) print(pd.concat([ser_obj1, ser_obj2, ser_obj3])) print(pd.concat([ser_obj1, ser_obj2, ser_obj3], axis=1))运行结果：# print(ser_obj1) 0 1 1 8 2 4 3 9 4 4 dtype: int64 # print(ser_obj2) 5 2 6 6 7 4 8 2 dtype: int64 # print(ser_obj3) 9 6 10 2 11 7 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3])) 0 1 1 8 2 4 3 9 4 4 5 2 6 6 7 4 8 2 9 6 10 2 11 7 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3], axis=1)) 0 1 2 0 1.0 NaN NaN 1 5.0 NaN NaN 2 3.0 NaN NaN 3 2.0 NaN NaN 4 4.0 NaN NaN 5 NaN 9.0 NaN 6 NaN 8.0 NaN 7 NaN 3.0 NaN 8 NaN 6.0 NaN 9 NaN NaN 2.0 10 NaN NaN 3.0 11 NaN NaN 3.02) index 有重复的情况示例代码：# index 有重复的情况 ser_obj1 = pd.Series(np.random.randint(0, 10, 5), index=range(5)) ser_obj2 = pd.Series(np.random.randint(0, 10, 4), index=range(4)) ser_obj3 = pd.Series(np.random.randint(0, 10, 3), index=range(3)) print(ser_obj1) print(ser_obj2) print(ser_obj3) print(pd.concat([ser_obj1, ser_obj2, ser_obj3]))运行结果：# print(ser_obj1) 0 0 1 3 2 7 3 2 4 5 dtype: int64 # print(ser_obj2) 0 5 1 1 2 9 3 9 dtype: int64 # print(ser_obj3) 0 8 1 7 2 9 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3])) 0 0 1 3 2 7 3 2 4 5 0 5 1 1 2 9 3 9 0 8 1 7 2 9 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3], axis=1, join='inner')) # join='inner' 将去除NaN所在的行或列 0 1 2 0 0 5 8 1 3 1 7 2 7 9 93) DataFrame合并时同时查看行索引和列索引有无重复示例代码：df_obj1 = pd.DataFrame(np.random.randint(0, 10, (3, 2)), index=['a', 'b', 'c'], columns=['A', 'B']) df_obj2 = pd.DataFrame(np.random.randint(0, 10, (2, 2)), index=['a', 'b'], columns=['C', 'D']) print(df_obj1) print(df_obj2) print(pd.concat([df_obj1, df_obj2])) print(pd.concat([df_obj1, df_obj2], axis=1, join='inner'))运行结果：# print(df_obj1) A B a 3 3 b 5 4 c 8 6 # print(df_obj2) C D a 1 9 b 6 8 # print(pd.concat([df_obj1, df_obj2])) A B C D a 3.0 3.0 NaN NaN b 5.0 4.0 NaN NaN c 8.0 6.0 NaN NaN a NaN NaN 1.0 9.0 b NaN NaN 6.0 8.0 # print(pd.concat([df_obj1, df_obj2], axis=1, join='inner')) A B C D a 3 3 1 9 b 5 4 6 8数据重构1. stack将列索引旋转为行索引，完成层级索引DataFrame->Series示例代码：import numpy as np import pandas as pd df_obj = pd.DataFrame(np.random.randint(0,10, (5,2)), columns=['data1', 'data2']) print(df_obj) stacked = df_obj.stack() print(stacked)运行结果：# print(df_obj) data1 data2 0 7 9 1 7 8 2 8 9 3 4 1 4 1 2 # print(stacked) 0 data1 7 data2 9 1 data1 7 data2 8 2 data1 8 data2 9 3 data1 4 data2 1 4 data1 1 data2 2 dtype: int642. unstack将层级索引展开Series->DataFrame认操作内层索引，即level=-1示例代码：# 默认操作内层索引 print(stacked.unstack()) # 通过level指定操作索引的级别 print(stacked.unstack(level=0))运行结果：# print(stacked.unstack()) data1 data2 0 7 9 1 7 8 2 8 9 3 4 1 4 1 2 # print(stacked.unstack(level=0)) 0 1 2 3 4 data1 7 7 8 4 1 data2 9 8 9 1 2数据转换一、 处理重复数据1 duplicated() 返回布尔型Series表示每行是否为重复行示例代码：import numpy as np import pandas as pd df_obj = pd.DataFrame({'data1' : ['a'] * 4 + ['b'] * 4, 'data2' : np.random.randint(0, 4, 8)}) print(df_obj) print(df_obj.duplicated())运行结果：# print(df_obj) data1 data2 0 a 3 1 a 2 2 a 3 3 a 3 4 b 1 5 b 0 6 b 3 7 b 0 # print(df_obj.duplicated()) 0 False 1 False 2 True 3 True 4 False 5 False 6 False 7 True dtype: bool2 drop_duplicates() 过滤重复行默认判断全部列可指定按某些列判断示例代码：print(df_obj.drop_duplicates()) print(df_obj.drop_duplicates('data2'))运行结果：# print(df_obj.drop_duplicates()) data1 data2 0 a 3 1 a 2 4 b 1 5 b 0 6 b 3 # print(df_obj.drop_duplicates('data2')) data1 data2 0 a 3 1 a 2 4 b 1 5 b 03. 根据map传入的函数对每行或每列进行转换Series根据map传入的函数对每行或每列进行转换示例代码：ser_obj = pd.Series(np.random.randint(0,10,10)) print(ser_obj) print(ser_obj.map(lambda x : x ** 2))运行结果：# print(ser_obj) 0 1 1 4 2 8 3 6 4 8 5 6 6 6 7 4 8 7 9 3 dtype: int64 # print(ser_obj.map(lambda x : x ** 2)) 0 1 1 16 2 64 3 36 4 64 5 36 6 36 7 16 8 49 9 9 dtype: int64二、数据替换replace根据值的内容进行替换示例代码：# 单个值替换单个值 print(ser_obj.replace(1, -100)) # 多个值替换一个值 print(ser_obj.replace([6, 8], -100)) # 多个值替换多个值 print(ser_obj.replace([4, 7], [-100, -200]))运行结果：# print(ser_obj.replace(1, -100)) 0 -100 1 4 2 8 3 6 4 8 5 6 6 6 7 4 8 7 9 3 dtype: int64 # print(ser_obj.replace([6, 8], -100)) 0 1 1 4 2 -100 3 -100 4 -100 5 -100 6 -100 7 4 8 7 9 3 dtype: int64 # print(ser_obj.replace([4, 7], [-100, -200])) 0 1 1 -100 2 8 3 6 4 8 5 6 6 6 7 -100 8 -200 9 3 dtype: int64