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pandas使用
作者：从前慢现在也慢 | 2024-06-02 18:36:26
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pandas使用
pandas是python字典和numpy的结合，它的每一行每一列都可以赋予一个名字，其真正的数据则是numpy数据。
它的主要数据结构是Series（一维数据）与DataFrame（二维数据）。
基本介绍
import pandas as pd import numpy as np # 定义一维数据 s = pd.Series([1,3,6,np.nan,44,1]) print(s) # 定义日期的数据 dates = pd.date_range('20220318',periods=6) print(dates) # 定义二维数据，指定行索引index和列索引columns df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d']) print(df) # 定义二维数据，使用默认索引 df1 = pd.DataFrame(np.arange(12).reshape(3,4)) print(df1) # 用字典定义DataFrame df2 = pd.DataFrame({ 'A':1., 'B':pd.Timestamp('20220318'), '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' }) print(df2) # DataFrame的属性 print(df2.dtypes) print(df2.index) print(df2.columns) print(df2.values) print(df2.describe()) # 计算values中数字数据的统计数据 print(df2.T) # 转置 # 按照index进行顺序 print(df2.sort_index(axis=1,ascending=False)) print(df2.sort_index(axis=0,ascending=False)) # 按照values进行排序 print(df2.sort_values(by='E'))
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结果：
0 1.0 1 3.0 2 6.0 3 NaN 4 44.0 5 1.0 dtype: float64 DatetimeIndex(['2022-03-18', '2022-03-19', '2022-03-20', '2022-03-21', '2022-03-22', '2022-03-23'], dtype='datetime64[ns]', freq='D') a b c d 2022-03-18 -0.686373 0.975663 -0.164654 -0.134341 2022-03-19 0.721421 2.056876 0.012457 -1.293365 2022-03-20 0.484431 -0.786625 1.069571 1.466806 2022-03-21 0.912695 0.488881 -0.733907 0.868177 2022-03-22 -0.406071 -2.308976 1.377417 -0.423213 2022-03-23 0.961852 -0.734838 1.221765 -2.290225 0 1 2 3 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 A B C D E F 0 1.0 2022-03-18 1.0 3 test foo 1 1.0 2022-03-18 1.0 3 train foo 2 1.0 2022-03-18 1.0 3 test foo 3 1.0 2022-03-18 1.0 3 train foo A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object Int64Index([0, 1, 2, 3], dtype='int64') Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object') [[1.0 Timestamp('2022-03-18 00:00:00') 1.0 3 'test' 'foo'] [1.0 Timestamp('2022-03-18 00:00:00') 1.0 3 'train' 'foo'] [1.0 Timestamp('2022-03-18 00:00:00') 1.0 3 'test' 'foo'] [1.0 Timestamp('2022-03-18 00:00:00') 1.0 3 'train' 'foo']] A C D count 4.0 4.0 4.0 mean 1.0 1.0 3.0 std 0.0 0.0 0.0 min 1.0 1.0 3.0 25% 1.0 1.0 3.0 50% 1.0 1.0 3.0 75% 1.0 1.0 3.0 max 1.0 1.0 3.0 0 ... 3 A 1 ... 1 B 2022-03-18 00:00:00 ... 2022-03-18 00:00:00 C 1 ... 1 D 3 ... 3 E test ... train F foo ... foo [6 rows x 4 columns] F E D C B A 0 foo test 3 1.0 2022-03-18 1.0 1 foo train 3 1.0 2022-03-18 1.0 2 foo test 3 1.0 2022-03-18 1.0 3 foo train 3 1.0 2022-03-18 1.0 A B C D E F 3 1.0 2022-03-18 1.0 3 train foo 2 1.0 2022-03-18 1.0 3 test foo 1 1.0 2022-03-18 1.0 3 train foo 0 1.0 2022-03-18 1.0 3 test foo A B C D E F 0 1.0 2022-03-18 1.0 3 test foo 2 1.0 2022-03-18 1.0 3 test foo 1 1.0 2022-03-18 1.0 3 train foo 3 1.0 2022-03-18 1.0 3 train foo
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pandas选择数据
import pandas as pd import numpy as np dates = pd.date_range('20220318',periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D']) print(df) # 索引列 print(df['A'],df.A) print(df[0:3],df['20220319':'20220321']) # 用label来筛选 loc print(df.loc['20220322']) print(df.loc[:,['A','B']]) # 用位置来筛选 iloc print(df.iloc[3,1]) print(df.iloc[3:5,1:3]) print(df.iloc[[1,3,5],1:3]) # 条件筛选 print(df[df.A<8])
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结果：
A B C D 2022-03-18 0 1 2 3 2022-03-19 4 5 6 7 2022-03-20 8 9 10 11 2022-03-21 12 13 14 15 2022-03-22 16 17 18 19 2022-03-23 20 21 22 23 2022-03-18 0 2022-03-19 4 2022-03-20 8 2022-03-21 12 2022-03-22 16 2022-03-23 20 Freq: D, Name: A, dtype: int32 2022-03-18 0 2022-03-19 4 2022-03-20 8 2022-03-21 12 2022-03-22 16 2022-03-23 20 Freq: D, Name: A, dtype: int32 A B C D 2022-03-18 0 1 2 3 2022-03-19 4 5 6 7 2022-03-20 8 9 10 11 A B C D 2022-03-19 4 5 6 7 2022-03-20 8 9 10 11 2022-03-21 12 13 14 15 A 16 B 17 C 18 D 19 Name: 2022-03-22 00:00:00, dtype: int32 A B 2022-03-18 0 1 2022-03-19 4 5 2022-03-20 8 9 2022-03-21 12 13 2022-03-22 16 17 2022-03-23 20 21 13 B C 2022-03-21 13 14 2022-03-22 17 18 B C 2022-03-19 5 6 2022-03-21 13 14 2022-03-23 21 22 A B C D 2022-03-18 0 1 2 3 2022-03-19 4 5 6 7
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pandas设置值
import pandas as pd import numpy as np dates = pd.date_range('20220318',periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D']) print(df) df.iloc[2,2] = 1111 df.loc['20220318'] = 2222 df.A[df.A>4] = 0 print(df) # 添加一列 df['F'] = np.nan print(df) # 虽然添加了一列数字，但是显示全是NaN df['E'] = pd.Series(np.array([1,2,3,4,5,6])) print(df) df['E'] = pd.Series(np.array([1,2,3,4,5,6]),index=dates) print(df)
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结果：
A B C D 2022-03-18 0 1 2 3 2022-03-19 4 5 6 7 2022-03-20 8 9 10 11 2022-03-21 12 13 14 15 2022-03-22 16 17 18 19 2022-03-23 20 21 22 23 A B C D 2022-03-18 0 2222 2222 2222 2022-03-19 4 5 6 7 2022-03-20 0 9 1111 11 2022-03-21 0 13 14 15 2022-03-22 0 17 18 19 2022-03-23 0 21 22 23 A B C D F 2022-03-18 0 2222 2222 2222 NaN 2022-03-19 4 5 6 7 NaN 2022-03-20 0 9 1111 11 NaN 2022-03-21 0 13 14 15 NaN 2022-03-22 0 17 18 19 NaN 2022-03-23 0 21 22 23 NaN A B C D F E 2022-03-18 0 2222 2222 2222 NaN NaN 2022-03-19 4 5 6 7 NaN NaN 2022-03-20 0 9 1111 11 NaN NaN 2022-03-21 0 13 14 15 NaN NaN 2022-03-22 0 17 18 19 NaN NaN 2022-03-23 0 21 22 23 NaN NaN A B C D F E 2022-03-18 0 2222 2222 2222 NaN 1 2022-03-19 4 5 6 7 NaN 2 2022-03-20 0 9 1111 11 NaN 3 2022-03-21 0 13 14 15 NaN 4 2022-03-22 0 17 18 19 NaN 5 2022-03-23 0 21 22 23 NaN 6
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pandas处理丢失数据
import pandas as pd import numpy as np dates = pd.date_range('20220318',periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D']) df.iloc[0,1] = np.nan df.iloc[1,2] = np.nan print(df) # 丢掉nan所在的列 print(df.dropna(axis=1,how='any')) # how={'any','all'},默认是'any' # 丢掉nan所在的行 print(df.dropna(axis=0,how='any')) # 用零填充nan print(df.fillna(value=0)) # 返回一个DataFrame，每个值都是bool，代表是否是nan print(df.isnull()) # 判断是否有缺失数据 print(np.any(df.isnull())==True)
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结果：
A B C D 2022-03-18 0 NaN 2.0 3 2022-03-19 4 5.0 NaN 7 2022-03-20 8 9.0 10.0 11 2022-03-21 12 13.0 14.0 15 2022-03-22 16 17.0 18.0 19 2022-03-23 20 21.0 22.0 23 A D 2022-03-18 0 3 2022-03-19 4 7 2022-03-20 8 11 2022-03-21 12 15 2022-03-22 16 19 2022-03-23 20 23 A B C D 2022-03-20 8 9.0 10.0 11 2022-03-21 12 13.0 14.0 15 2022-03-22 16 17.0 18.0 19 2022-03-23 20 21.0 22.0 23 A B C D 2022-03-18 0 0.0 2.0 3 2022-03-19 4 5.0 0.0 7 2022-03-20 8 9.0 10.0 11 2022-03-21 12 13.0 14.0 15 2022-03-22 16 17.0 18.0 19 2022-03-23 20 21.0 22.0 23 A B C D 2022-03-18 False True False False 2022-03-19 False False True False 2022-03-20 False False False False 2022-03-21 False False False False 2022-03-22 False False False False 2022-03-23 False False False False True
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pandas导入导出
import pandas as pd
# 会自动加上index
data = pd.read_csv('student.csv')
print(data)
data.to_pickle('student.pickle')
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结果：
原始数据如下图所示：
Student ID name age gender
0 1100 Kelly 22 Female
1 1101 Clo 21 Female
2 1102 Tilly 22 Female
3 1103 Tony 24 Male
4 1104 David 20 Male
5 1105 Catty 22 Female
6 1106 M 3 Female
7 1107 N 43 Male
8 1108 A 13 Male
9 1109 S 12 Male
10 1110 David 33 Male
11 1111 Dw 3 Female
12 1112 Q 23 Male
13 1113 W 21 Female
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pandas合并concat
import pandas as pd import numpy as np # concatenating df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d']) df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d']) df3 = pd.DataFrame(np.ones((3,4))*2,columns=['a','b','c','d']) print(df1) print(df2) print(df3) # 保留原始index res = pd.concat([df1,df2,df3],axis=0) print(res) # 忽略原始index，重新赋予index res = pd.concat([df1,df2,df3],axis=0,ignore_index=True) print(res) # join,['inner','outer'] df1 = pd.DataFrame(np.ones((3,4))*0,index=[1,2,3],columns=['a','b','c','d']) df2 = pd.DataFrame(np.ones((3,4))*1,index=[2,3,4],columns=['b','c','d','e']) print(df1) print(df2) # 相当于两个的并集，不相互重合的地方会用nan填充 res = pd.concat([df1,df2],join='outer',ignore_index=True) # 默认是outer填充 print(res) # 相当于两个的交集 res = pd.concat([df1,df2],join='inner',ignore_index=True) print(res) # 新版本pandas中按列合并只能用merge df1 = pd.DataFrame(np.ones((3,4))*0,index=[1,2,3],columns=['a','b','c','d']) df2 = pd.DataFrame(np.ones((3,4))*1,index=[2,3,4],columns=['b','c','d','e']) # how {'inner','outer','left','right'} res = pd.merge(df1,df2,how='left',left_index=True,right_index=True) print(res) # append 加数据 df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d']) df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d']) df3 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d']) res = df1.append([df2,df3],ignore_index=True) print(res) s1 = pd.Series([1,2,3,4],index=['a','b','c','d']) res = df1.append(s1,ignore_index=True) print(res)
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结果：
a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 a b c d 0 1.0 1.0 1.0 1.0 1 1.0 1.0 1.0 1.0 2 1.0 1.0 1.0 1.0 a b c d 0 2.0 2.0 2.0 2.0 1 2.0 2.0 2.0 2.0 2 2.0 2.0 2.0 2.0 a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0 1.0 1.0 1.0 1.0 1 1.0 1.0 1.0 1.0 2 1.0 1.0 1.0 1.0 0 2.0 2.0 2.0 2.0 1 2.0 2.0 2.0 2.0 2 2.0 2.0 2.0 2.0 a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 6 2.0 2.0 2.0 2.0 7 2.0 2.0 2.0 2.0 8 2.0 2.0 2.0 2.0 a b c d 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 b c d e 2 1.0 1.0 1.0 1.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 a b c d e 0 0.0 0.0 0.0 0.0 NaN 1 0.0 0.0 0.0 0.0 NaN 2 0.0 0.0 0.0 0.0 NaN 3 NaN 1.0 1.0 1.0 1.0 4 NaN 1.0 1.0 1.0 1.0 5 NaN 1.0 1.0 1.0 1.0 b c d 0 0.0 0.0 0.0 1 0.0 0.0 0.0 2 0.0 0.0 0.0 3 1.0 1.0 1.0 4 1.0 1.0 1.0 5 1.0 1.0 1.0 a b_x c_x d_x b_y c_y d_y e 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 6 1.0 1.0 1.0 1.0 7 1.0 1.0 1.0 1.0 8 1.0 1.0 1.0 1.0 a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 2.0 3.0 4.0
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pandas合并merge
import pandas as pd import numpy as np left = pd.DataFrame({'key':['K0','K1','K2','K3'], 'A':['A0','A1','A2','A3'], 'B':['B0','B1','B2','B3'],}) right = pd.DataFrame({'key':['K0','K1','K2','K3'], 'C':['C0','C1','C2','C3'], 'D':['D0','D1','D2','D3'],}) print(left) print(right) # merging two df by key/keys res = pd.merge(left,right,on='key') print(res) # consider two keys left = pd.DataFrame({'key1':['K0','K0','K1','K2'], 'key2':['K0','K1','K0','K1'], 'A':['A0','A1','A2','A3'], 'B':['B0','B1','B2','B3'],}) right = pd.DataFrame({'key1':['K0','K1','K1','K2'], 'key2':['K0','K0','K0','K0'], 'C':['C0','C1','C2','C3'], 'D':['D0','D1','D2','D3'],}) print(left) print(right) # left有一对(K1,K0),right有两对(K1,K0),inner方式合并就是把A那一对复制两遍进行合并 # how {'inner','outer','left','right'}, 默认是inner res = pd.merge(left,right,on=['key1','key2'],how='inner') print(res) # how='outer',没有的就用nan res = pd.merge(left,right,on=['key1','key2'],how='outer') print(res) # how='right',基于right进行合并，若left没有的就填充nan res = pd.merge(left,right,on=['key1','key2'],how='right') print(res) df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']}) df2 = pd.DataFrame({'col1':[1,2,2],'col_left':[2,2,2]}) print(df1) print(df2) # given the indicator a custom name res = pd.merge(df1,df2,on='col1',how='outer',indicator=True) print(res) res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column') print(res) # merge by index left = pd.DataFrame({'A':['A0','A1','A2'], 'B':['B0','B1','B2'],}, index=['K0','K1','K2']) right = pd.DataFrame({'C':['C0','C1','C2'], 'D':['D0','D1','D2'],}, index=['K0','K1','K2']) print(left) print(right) # left_index and right_index res = pd.merge(left,right,left_index=True,right_index=True,how='outer') print(res) # handle overlapping boys = pd.DataFrame({'k':['K0','K1','K2'],'age':[1,2,3]}) girls = pd.DataFrame({'k':['K0','K0','K3'],'age':[4,5,6]}) print(boys) print(girls) res = pd.merge(boys,girls,on='k',how='inner') print(res) res = pd.merge(boys,girls,on='k',how='inner',suffixes=['_boy','_girl']) # 设置同名key的后缀 print(res)
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结果：
key A B 0 K0 A0 B0 1 K1 A1 B1 2 K2 A2 B2 3 K3 A3 B3 key C D 0 K0 C0 D0 1 K1 C1 D1 2 K2 C2 D2 3 K3 C3 D3 key A B C D 0 K0 A0 B0 C0 D0 1 K1 A1 B1 C1 D1 2 K2 A2 B2 C2 D2 3 K3 A3 B3 C3 D3 key1 key2 A B 0 K0 K0 A0 B0 1 K0 K1 A1 B1 2 K1 K0 A2 B2 3 K2 K1 A3 B3 key1 key2 C D 0 K0 K0 C0 D0 1 K1 K0 C1 D1 2 K1 K0 C2 D2 3 K2 K0 C3 D3 key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K1 K0 A2 B2 C1 D1 2 K1 K0 A2 B2 C2 D2 key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K0 K1 A1 B1 NaN NaN 2 K1 K0 A2 B2 C1 D1 3 K1 K0 A2 B2 C2 D2 4 K2 K1 A3 B3 NaN NaN 5 K2 K0 NaN NaN C3 D3 key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K1 K0 A2 B2 C1 D1 2 K1 K0 A2 B2 C2 D2 3 K2 K0 NaN NaN C3 D3 col1 col_left 0 0 a 1 1 b col1 col_left 0 1 2 1 2 2 2 2 2 col1 col_left_x col_left_y _merge 0 0 a NaN left_only 1 1 b 2.0 both 2 2 NaN 2.0 right_only 3 2 NaN 2.0 right_only col1 col_left_x col_left_y indicator_column 0 0 a NaN left_only 1 1 b 2.0 both 2 2 NaN 2.0 right_only 3 2 NaN 2.0 right_only A B K0 A0 B0 K1 A1 B1 K2 A2 B2 C D K0 C0 D0 K1 C1 D1 K2 C2 D2 A B C D K0 A0 B0 C0 D0 K1 A1 B1 C1 D1 K2 A2 B2 C2 D2 k age 0 K0 1 1 K1 2 2 K2 3 k age 0 K0 4 1 K0 5 2 K3 6 k age_x age_y 0 K0 1 4 1 K0 1 5 k age_boy age_girl 0 K0 1 4 1 K0 1 5
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pandas plot 绘图
import pandas as pd import numpy as np import matplotlib.pyplot as plt # Series data = pd.Series(np.random.randn(1000),index=np.arange(1000)) data = data.cumsum() data.plot() # DataFrame data = pd.DataFrame(np.random.randn(1000,4), index=np.arange(1000), columns=list("ABCD")) data = data.cumsum() data.plot() # plot methods: 'bar', 'hist','box','kde','area','scatter','hexbin','pie' ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class 1') data.plot.scatter(x='A',y='C',color='DarkGreen',label='Class 2',ax=ax) plt.show()
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结果：
补充
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