Python练手，pandas
发表于：2025-01-25 作者：千家信息网编辑
千家信息网最后更新 2025年01月25日，'''http://pandas.pydata.org/pandas-docs/stable/10min.html numpy的主要数据结构是ndarry pandas的主要数据结构是Se
千家信息网最后更新 2025年01月25日Python练手，pandas
'''http://pandas.pydata.org/pandas-docs/stable/10min.html numpy的主要数据结构是ndarry pandas的主要数据结构是Series、DataFrame'''import pandas as pdimport numpy as npimport matplotlib.pyplot as plt df1 = pd.DataFrame(np.array(range(101,125)).reshape(6,4), index=range(6), columns=list('ABCD'))print(df1)# A B C D# 0 101 102 103 104# 1 105 106 107 108# 2 109 110 111 112# 3 113 114 115 116# 4 117 118 119 120# 5 121 122 123 124df2 = pd.DataFrame({'custID':['C0001','C0002','C0004','C0004','C0004','C0003'], 'accountID':pd.Series(['6214C000101', '6214C000201', '6214C000401', '6214C000403', '6214C000402', '6214C000301'],index=range(6),dtype='str'), 'tradeDate':pd.Series(['2018-01-18 14:00:00', '2018-01-18 14:00:00', '2018-01-18 14:00:01', '2018-01-18 14:00:03', '2018-01-18 14:00:02', '2018-01-18 14:00:00'],index=range(6),dtype='str'), 'tradeAmt':pd.Series([100.0, 100.0, 101.0, 103.0, 102.0, 100.0],index=range(6),dtype='float'), 'tradeDesc':'xxxxxx', 'mark':pd.Categorical(["row1","row2","row3","row4","row5","row6"])}, index=range(6)) #注意：表DateFrame与列Series的索引保持一致。DateFrame的真实index默认是从0开始的，这里设置的其实是index的标签，如果自定义了DateFrame的index（标签），假如某列是Series，那么Series的index也必须保持一致，否则会错位。print(df2)# accountID custID mark tradeAmt tradeDate tradeDesc# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxxprint(df2.dtypes)# accountID object# custID object# mark category# tradeAmt float64# tradeDate object# tradeDesc object# dtype: objectprint(df2.index)# RangeIndex(start=0, stop=6, step=1)print(df2.columns)# Index(['accountID', 'custID', 'mark', 'tradeAmt', 'tradeDate', 'tradeDesc'], dtype='object')print(df2.values)# [['6214C000101' 'C0001' 'row1' 100.0 '2018-01-18 14:00:00' 'xxxxxx']# ['6214C000201' 'C0002' 'row2' 100.0 '2018-01-18 14:00:00' 'xxxxxx']# ['6214C000401' 'C0004' 'row3' 101.0 '2018-01-18 14:00:01' 'xxxxxx']# ['6214C000403' 'C0004' 'row4' 103.0 '2018-01-18 14:00:03' 'xxxxxx']# ['6214C000402' 'C0004' 'row5' 102.0 '2018-01-18 14:00:02' 'xxxxxx']# ['6214C000301' 'C0003' 'row6' 100.0 '2018-01-18 14:00:00' 'xxxxxx']]print(df2.head(2))# accountID custID mark tradeAmt tradeDate tradeDesc# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxxprint(df2.tail(2))# accountID custID mark tradeAmt tradeDate tradeDesc# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxxprint(df2.describe()) #统计，但仅限数值的列，非数值的列不会输出统计# tradeAmt# count 6.000000# mean 101.000000# std 1.264911# min 100.000000# 25% 100.000000# 50% 100.500000# 75% 101.750000# max 103.000000print(df2.T)# 0 1 2 \# accountID 6214C000101 6214C000201 6214C000401 # custID C0001 C0002 C0004 # mark row1 row2 row3 # tradeAmt 100 100 101 # tradeDate 2018-01-18 14:00:00 2018-01-18 14:00:00 2018-01-18 14:00:01 # tradeDesc xxxxxx xxxxxx xxxxxx # # 3 4 5 # accountID 6214C000403 6214C000402 6214C000301 # custID C0004 C0004 C0003 # mark row4 row5 row6 # tradeAmt 103 102 100 # tradeDate 2018-01-18 14:00:03 2018-01-18 14:00:02 2018-01-18 14:00:00 # tradeDesc xxxxxx xxxxxx xxxxxx print('------------------------------------------------------------------------------------')print(df2.sort_values(by='tradeDate',ascending=False)) #排序 按指定列的值 降序# accountID custID mark tradeAmt tradeDate tradeDesc# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxxprint(df2.sort_values(by=['custID','tradeDate'],ascending=[True,False])) #联合排序# accountID custID mark tradeAmt tradeDate tradeDesc# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxxprint(df2.sort_index(axis=0,ascending=False)) #索引排序 按照行的索引# accountID custID mark tradeAmt tradeDate tradeDesc# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxxprint(df2.sort_index(axis=1,ascending=True)) #索引排序 按照列的索引（默认是按照列名生成的行索引）# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxxprint('------------------------------------------------------------------------------------')''' iloc按索引查找，loc按标签查找 iat按索引查找，iat按标签查找'''print(df2['custID'])# 0 C0001# 1 C0002# 2 C0004# 3 C0004# 4 C0004# 5 C0003# Name: custID, dtype: objectprint(df2[0:4]) #切片 按行索引# accountID custID mark tradeAmt tradeDate tradeDesc# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxxprint(df2[1:4]) #切片 按行索引# accountID custID mark tradeAmt tradeDate tradeDesc# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxxprint(df2.loc[1,'accountID']) #按行列标签查找，不是按行列索引查找# 6214C000201print(df2.iloc[3]) #第4行# accountID 6214C000403# custID C0004# mark row4# tradeAmt 103# tradeDate 2018-01-18 14:00:03# tradeDesc xxxxxx# Name: 3, dtype: objectprint(df2.iloc[3,4]) #第4行 第5列# 2018-01-18 14:00:03print(df2.iloc[3:4]) #第4至5行（不含第5行）# accountID custID mark tradeAmt tradeDate tradeDesc# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxxprint(df2.iloc[3:5,1:3]) #第4、5行，第2、3列（列索引如果没有自定义，是按列名排序自动生成的）# custID mark# 3 C0004 row4# 4 C0004 row5print(df2.iloc[[3,4],[1,2]]) #第4、5行，第2、3列# custID mark# 3 C0004 row4# 4 C0004 row5print(df2.iloc[3:5,:]) #第4、5行，所有列# accountID custID mark tradeAmt tradeDate tradeDesc# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxxprint(df2.iloc[:,1:3]) #所有行，第2、3列# custID mark# 0 C0001 row1# 1 C0002 row2# 2 C0004 row3# 3 C0004 row4# 4 C0004 row5# 5 C0003 row6print(df2[df2.tradeAmt > 101.0]) #筛选# accountID custID mark tradeAmt tradeDate tradeDesc# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxxprint('------------------------------------------------------------------------------------')df3 = df2.copy()df3["custID"] = ["NEW","NEW","NEW","NEW","NEW","NEW"] # 更新 整列df3.loc[:,'tradeAmt'] = range(len(df3)) #更新 按行列标签查找df3.at[range(7)[1],'accountID'] = '===========' # 更新 按行列标签查找df3.iat[0,0] = '+++++++++++' # 更新 按行列索引查找# df3[df3.tradeDate == '2018-01-18 14:00:03'] = -df3 #找出符合条件的行，然后取反，如果所有字段都是数值的话是可以的print(df3)# accountID custID mark tradeAmt tradeDate tradeDesc# 0 +++++++++++ NEW row1 0 2018-01-18 14:00:00 xxxxxx# 1 =========== NEW row2 1 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 NEW row3 2 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 NEW row4 3 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 NEW row5 4 2018-01-18 14:00:02 xxxxxx# 5 6214C000301 NEW row6 5 2018-01-18 14:00:00 xxxxxxprint('------------------------------------------------------------------------------------')df4 = df2.reindex(index=range(4), columns=['custID','accountID','tradeAmt']) #重新组合 抽取df4.loc[0:1,'tradeAmt'] = 200 #如果该列存在，则更新df4.loc[0:1,'newColumn'] = 1 #如果该列不存在，则新增列print(df4)# custID accountID tradeAmt newColumn# 0 C0001 6214C000101 200.0 1.0# 1 C0002 6214C000201 200.0 1.0# 2 C0004 6214C000401 101.0 NaN# 3 C0004 6214C000403 103.0 NaNprint(df4.dropna(how='any')) #过滤所有包含空值的行# custID accountID tradeAmt newColumn# 0 C0001 6214C000101 200.0 1.0# 1 C0002 6214C000201 200.0 1.0print(df4.fillna(value=999)) #填充空值# custID accountID tradeAmt newColumn# 0 C0001 6214C000101 200.0 1.0# 1 C0002 6214C000201 200.0 1.0# 2 C0004 6214C000401 101.0 999.0# 3 C0004 6214C000403 103.0 999.0print(pd.isnull(df4)) #判断空值# custID accountID tradeAmt newColumn# 0 False False False False# 1 False False False False# 2 False False False True# 3 False False False Trueprint('------------------------------------------------------------------------------------')print(df2)# accountID custID mark tradeAmt tradeDate tradeDesc# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxxprint(df2.mean())# tradeAmt 101.0# dtype: float64s = pd.Series([1,3,5,np.nan,6,8], index=range(6)).shift(2) # 向后移动几行，前面置空print(s)# 0 NaN# 1 1.0# 2 3.0# 3 5.0# 4 NaN# 5 6.0# dtype: float64print(df2.shift(2))# accountID custID mark tradeAmt tradeDate tradeDesc# 0 NaN NaN NaN NaN NaN NaN# 1 NaN NaN NaN NaN NaN NaN# 2 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 3 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 4 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 5 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxxprint('------------------------------------------------------------------------------------')print(df2.apply(lambda x: max(x))) #列函数 lambda或者function都可以# accountID 6214C000403# custID C0004# mark row6# tradeAmt 103# tradeDate 2018-01-18 14:00:03# tradeDesc xxxxxx# dtype: objectprint('------------------------------------------------------------------------------------')print(df2["custID"].value_counts()) #类似 group by count # C0004 3# C0001 1# C0002 1# C0003 1# Name: custID, dtype: int64print('------------------------------------------------------------------------------------')print(df2["mark"].str.upper()) #大小写转换# 0 ROW1# 1 ROW2# 2 ROW3# 3 ROW4# 4 ROW5# 5 ROW6# Name: mark, dtype: objectprint('------------------------------------------------------------------------------------')df5 = pd.DataFrame(np.random.randn(9,3))print(df5)# 0 1 2# 0 1.303158 -0.125934 -0.205285# 1 0.760388 -1.004298 1.143800# 2 2.063722 0.229955 0.020368# 3 -2.024974 0.307957 -0.579090# 4 -1.571883 0.260561 -0.884209# 5 2.465572 -1.001873 1.243028# 6 0.025388 -0.372608 1.431214# 7 -0.079416 -0.401075 -0.973337# 8 -1.088755 -1.947188 -1.100827pieces = [df5[:2],df5[5:6],df5[7:]] #头、中间、尾，切几块拼起来print(pieces)# [ 0 1 2# 0 1.303158 -0.125934 -0.205285# 1 0.760388 -1.004298 1.143800, 0 1 2# 5 2.465572 -1.001873 1.243028, 0 1 2 #index重复打印了几次# 7 -0.079416 -0.401075 -0.973337# 8 -1.088755 -1.947188 -1.100827]print(pd.concat(pieces)) #包含# 0 1 2# 0 1.303158 -0.125934 -0.205285# 1 0.760388 -1.004298 1.143800# 5 2.465572 -1.001873 1.243028# 7 -0.079416 -0.401075 -0.973337# 8 -1.088755 -1.947188 -1.100827print('------------------------------------------------------------------------------------')df_left = pd.DataFrame({'key':['001','002','007'],'val':['999','1','2']})df_right = pd.DataFrame({'key':['001','002','009'],'key2':['001','002','009'],'val':['999','3','4']})print(df_left)# key val# 0 001 999# 1 002 1# 2 007 2print(df_right)# key key2 val# 0 001 001 999# 1 002 002 3# 2 009 009 4print( pd.merge(df_left, df_right,how='inner', on='key') ) #内关联# key val_x key2 val_y# 0 001 999 001 999# 1 002 1 002 3print( pd.merge(df_left, df_right, how='inner', left_on='key',right_on='key2') ) #内关联 不同字段# key_x val_x key_y key2 val_y# 0 001 999 001 001 999# 1 002 1 002 002 3print( pd.merge(df_left, df_right,how='inner', on=['key','val']) ) #内关联 多字段# key val key2# 0 001 999 001print( pd.merge(df_left, df_right, how='left', on='key') ) #左外关联# key val_x key2 val_y# 0 001 999 001 999# 1 002 1 002 3# 2 007 2 NaN NaNprint( pd.merge(df_left, df_right, how='right', on='key') ) #右外关联# key val_x key2 val_y# 0 001 999 001 999# 1 002 1 002 3# 2 009 NaN 009 4print('------------------------------------------------------------------------------------')print(df2.append(df2[:3],ignore_index=True)) #对原表做行切片，再追加到原表，追加的时候忽略切片的索引标签，索引自动重新编排标签# accountID custID mark tradeAmt tradeDate tradeDesc# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxx# 6 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx （这行是追加的）# 7 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx （这行是追加的）# 8 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx （这行是追加的）print(df2.append(df2[:3],ignore_index=False)) #追加之后，保留切片的索引标签，发现了吗，索引标签是允许重复的# accountID custID mark tradeAmt tradeDate tradeDesc# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx# 3 6214C000403 C0004 row4 103.0 2018-01-18 14:00:03 xxxxxx# 4 6214C000402 C0004 row5 102.0 2018-01-18 14:00:02 xxxxxx# 5 6214C000301 C0003 row6 100.0 2018-01-18 14:00:00 xxxxxx# 0 6214C000101 C0001 row1 100.0 2018-01-18 14:00:00 xxxxxx （这行是追加的）# 1 6214C000201 C0002 row2 100.0 2018-01-18 14:00:00 xxxxxx （这行是追加的）# 2 6214C000401 C0004 row3 101.0 2018-01-18 14:00:01 xxxxxx （这行是追加的）print('------------------------------------------------------------------------------------')tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', #zip()函数，将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])) index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) # 多索引标签MultiIndexdf6 = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])print(df6)# A B# first second # bar one -0.101234 -0.956210# two -0.480354 1.308950# baz one 0.943706 0.976480# two -0.788852 -1.556547# foo one 0.997527 -0.337391# two -0.191448 -0.083129# qux one -0.919527 -0.414051# two -0.579727 1.595290stacked = df6.stack() # 把"行列表结构"变成"堆栈结构"（姑且这样称呼它），把列标签追加到行标签之后print(stacked) # first second # bar one A -0.101234# B -0.956210# two A -0.480354# B 1.308950# baz one A 0.943706# B 0.976480# two A -0.788852# B -1.556547# foo one A 0.997527# B -0.337391# two A -0.191448# B -0.083129# qux one A -0.919527# B -0.414051# two A -0.579727# B 1.595290print(stacked["bar"]["one"]["A"]) # "堆栈结构"的好处是，你可以这样访问数据，可以想象"堆栈结构"其实就是多层数组# dtype: float64# -0.101233870095unstacked = stacked.unstack() # 还原回去，把"堆栈结构"变成"行列表结构"，把行标签变成列print(unstacked)# A B# first second # bar one -0.101234 -0.956210# two -0.480354 1.308950# baz one 0.943706 0.976480# two -0.788852 -1.556547# foo one 0.997527 -0.337391# two -0.191448 -0.083129# qux one -0.919527 -0.414051# two -0.579727 1.595290unstacked_unstacked_0 = unstacked.unstack(0) #还能继续吧行标签变成列标签print(unstacked_unstacked_0)# A B # first bar baz foo qux bar baz foo qux # second # one -0.101234 0.943706 0.997527 -0.919527 -0.95621 0.976480 -0.337391 -0.414051 # two -0.480354 -0.788852 -0.191448 -0.579727 1.30895 -1.556547 -0.083129 1.595290 unstacked_unstacked_1 = unstacked.unstack(1) #还能继续吧行标签变成列标签 把第2个标签变成列标签print(unstacked_unstacked_1)# A B # second one two one two# first # bar -0.101234 -0.480354 -0.956210 1.308950# baz 0.943706 -0.788852 0.976480 -1.556547# foo 0.997527 -0.191448 -0.337391 -0.083129# qux -0.919527 -0.579727 -0.414051 1.595290print('------------------------------------------------------------------------------------')df7 = 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)})print(df7)# A B C D E# 0 one A foo -0.516297 -0.860641# 1 one B foo -1.560483 -1.647366# 2 two C foo 1.124756 0.329971# 3 three A bar -0.312954 0.040263# 4 one B bar -1.355079 0.358829# 5 one C bar 0.749617 0.978513# 6 two A foo -2.173830 0.434789# 7 three B foo -1.070213 0.641253# 8 one C foo -0.515032 0.127273# 9 one A bar -1.408970 0.025128# 10 two B bar -0.390044 0.060392# 11 three C bar 0.067667 0.676595print( pd.pivot_table(df7, values='D', index=['A', 'B'], columns=['C']) ) #透视表# C bar foo# A B # one A -1.408970 -0.516297# B -1.355079 -1.560483# C 0.749617 -0.515032# three A -0.312954 NaN# B NaN -1.070213# C 0.067667 NaN# two A NaN -2.173830# B -0.390044 NaN# C NaN 1.124756print('------------------------------------------------------------------------------------')rng = pd.date_range('1/1/2012', periods=10, freq='min') #看结果，是个时间索引DatetimeIndexprint(rng)# DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:01:00',# '2012-01-01 00:02:00', '2012-01-01 00:03:00',# '2012-01-01 00:04:00', '2012-01-01 00:05:00',# '2012-01-01 00:06:00', '2012-01-01 00:07:00',# '2012-01-01 00:08:00', '2012-01-01 00:09:00'],# dtype='datetime64[ns]', freq='T')ts = pd.Series(range(10), index=rng) # 时间序列数据print(ts)# 2012-01-01 00:00:00 0# 2012-01-01 00:01:00 1# 2012-01-01 00:02:00 2# 2012-01-01 00:03:00 3# 2012-01-01 00:04:00 4# 2012-01-01 00:05:00 5# 2012-01-01 00:06:00 6# 2012-01-01 00:07:00 7# 2012-01-01 00:08:00 8# 2012-01-01 00:09:00 9# Freq: T, dtype: int32print( ts.resample('5Min').sum() ) #resample()是对时间序列数据进行重新采样的便捷方法# 2012-01-01 00:00:00 10# 2012-01-01 00:05:00 35# Freq: 5T, dtype: int32ts_utc = ts.tz_localize('UTC') #改变时区标准 UTC世界时 GMT格里尼治时print( ts_utc )# 2012-01-01 00:00:00+00:00 0# 2012-01-01 00:01:00+00:00 1# 2012-01-01 00:02:00+00:00 2# 2012-01-01 00:03:00+00:00 3# 2012-01-01 00:04:00+00:00 4# 2012-01-01 00:05:00+00:00 5# 2012-01-01 00:06:00+00:00 6# 2012-01-01 00:07:00+00:00 7# 2012-01-01 00:08:00+00:00 8# 2012-01-01 00:09:00+00:00 9# Freq: T, dtype: int32print( ts_utc.tz_convert('US/Eastern') ) #时区转换# 2011-12-31 19:00:00-05:00 0# 2011-12-31 19:01:00-05:00 1# 2011-12-31 19:02:00-05:00 2# 2011-12-31 19:03:00-05:00 3# 2011-12-31 19:04:00-05:00 4# 2011-12-31 19:05:00-05:00 5# 2011-12-31 19:06:00-05:00 6# 2011-12-31 19:07:00-05:00 7# 2011-12-31 19:08:00-05:00 8# 2011-12-31 19:09:00-05:00 9# Freq: T, dtype: int32print( ts.to_period() ) #时间序列显示格式，只显示到你定义的单位 # 2012-01-01 00:00 0# 2012-01-01 00:01 1# 2012-01-01 00:02 2# 2012-01-01 00:03 3# 2012-01-01 00:04 4# 2012-01-01 00:05 5# 2012-01-01 00:06 6# 2012-01-01 00:07 7# 2012-01-01 00:08 8# 2012-01-01 00:09 9# Freq: T, dtype: int32print( ts.to_period().to_timestamp() ) #时间序列显示格式，标准时间格式# 2012-01-01 00:00:00 0# 2012-01-01 00:01:00 1# 2012-01-01 00:02:00 2# 2012-01-01 00:03:00 3# 2012-01-01 00:04:00 4# 2012-01-01 00:05:00 5# 2012-01-01 00:06:00 6# 2012-01-01 00:07:00 7# 2012-01-01 00:08:00 8# 2012-01-01 00:09:00 9# Freq: T, dtype: int32print('------------------------------------------------------------------------------------')df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})df["grade"] = df["raw_grade"].astype("category") #创建新的列，支持category类型数据（category是一种类别标签）print( df["grade"] )# 0 a# 1 b# 2 b# 3 a# 4 a# 5 e# Name: grade, dtype: categorydf["grade"].cat.categories = ["very good", "good", "very bad"]df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) #重新定义类别，覆盖原来的类别print( df["grade"] )# 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]print( df.groupby("grade").size() ) #按类别统计# grade# very bad 1# bad 0# medium 0# good 2# very good 3# dtype: int64print('------------------------------------------------------------------------------------')ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)) #1000日的时间序列+随机数ts = ts.cumsum() #累加统计print(ts)ts.plot() #有的环境到这步就显式了plt.show() #有的要导入matplotlib.pyplot模块，这样开启图像显示#图像是一条曲线，X轴：1000日，y轴：每日的累加统计结果df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,columns=['A', 'B', 'C', 'D']) #时间序列的索引标签，4列的表df = df.cumsum() #每列的累加统计df.plot()plt.show()#图像是4条曲线，X轴：1000日，y轴：每日的累加统计结果
标签
索引
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序列
数据
时间序列
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