个人见解：像Excel
import numpy as np import pandas as pd
print(np.array([1,2,3,4,5]))
[1 2 3 4 5]
print(np.arange(1,10,1))
[1 2 3 4 5 6 7 8 9]
print(np.array(np.arange(10)))
[0 1 2 3 4 5 6 7 8 9]
myList = [[0,1],[1,2],[2,3]] print(np.array(myList))
[[0 1] [1 2] [2 3]]
myList1= [[0,5],[1,6],[2,7]] print(np.array(myList1))
[[0 5] [1 6] [2 7]]
#相加 List1 = np.array(myList) List2 = np.array(myList1) print(List1+List2)
[[ 0 6] [ 2 8] [ 4 10]]
#合并 print(np.concatenate((List1,List2),axis=1))
[[0 1 0 5] [1 2 1 6] [2 3 2 7]]
print(np.hstack((List1,List2)))
[[0 1 0 5] [1 2 1 6] [2 3 2 7]]
#索引 ser1 = np.array([1,2,3,4,5]) pd1 = pd.Series(ser1,index=np.arange(5))
print(pd1)
0 1 1 2 2 3 3 4 4 5 dtype: int32
ser2 = np.array([6,7,8,9,10]) pd2 = pd.Series(ser2,index=np.arange(5)) print(pd2)
0 6 1 7 2 8 3 9 4 10 dtype: int32
print(pd.DataFrame(ser2,index=np.arange(5),columns=['apple']))
apple 0 6 1 7 2 8 3 9 4 10
print(pd.Series([3,2,0,1],index=np.arange(4))) print(pd.Series([0,3,7,2],index=np.arange(4)))
0 3 1 2 2 0 3 1 dtype: int64 0 0 1 3 2 7 3 2 dtype: int64
myList2 = [[3,0],[2,3],[0,7],[1,2]] print(pd.DataFrame(myList2,index=np.arange(4),columns=['apples','oranges']))
apples oranges 0 3 0 1 2 3 2 0 7 3 1 2
import pandas as pd from pandas import Series,DataFrame
x1 = Series([1,2,3,4]) x2 = Series(data=[1,2,3,4],index=['a','b','c','d']) mydata = {'a':1,'b':2,'c':3,'d':4}#使用字典创建 x3 = Series(mydata)
print(x1)
0 1 1 2 2 3 3 4 dtype: int64
print(x2)
a 1 b 2 c 3 d 4 dtype: int64
print(x3)
a 1 b 2 c 3 d 4 dtype: int64
print(x3.count())
4
print(x3.max())
4
print(x3.min())
1
print(x3.mean())
2.5
print(x3.sum())
10
print(x3.median())
2.5
print(x3.argmax())
3
print(x3.var())
1.6666666666666667
print(x3.describe())
count 4.000000 mean 2.500000 std 1.290994 min 1.000000 25% 1.750000 50% 2.500000 75% 3.250000 max 4.000000 dtype: float64
df1 = DataFrame({'name':['zhangfei','guanyu','a','b','c'],'data1':range(1,6)})
df2 = DataFrame({'name':['zhangfei','guanyu','A','B','C'],'data2':range(1,6)})
df3 = pd.merge(df1,df2,on='name')
print(df1) print(df2) print(df3)
name data1
0 zhangfei 1
1 guanyu 2
2 a 3
3 b 4
4 c 5
name data2
0 zhangfei 1
1 guanyu 2
2 A 3
3 B 4
4 C 5
name data1 data2
0 zhangfei 1 1
1 guanyu 2 2
df3 = pd.merge(df1,df2,how='inner') print(df3)
name data1 data2 0 zhangfei 1 1 1 guanyu 2 2
#第一个 df3 = pd.merge(df1,df2,how='left') print(df3)
name data1 data2 0 zhangfei 1 1.0 1 guanyu 2 2.0 2 a 3 NaN 3 b 4 NaN 4 c 5 NaN
#第二个 df3 = pd.merge(df1,df2,how='right') print(df3)
name data1 data2 0 zhangfei 1.0 1 1 guanyu 2.0 2 2 A NaN 3 3 B NaN 4 4 C NaN 5
#所有 df3 = pd.merge(df1,df2,how='outer') print(df3)
name data1 data2 0 zhangfei 1.0 1.0 1 guanyu 2.0 2.0 2 a 3.0 NaN 3 b 4.0 NaN 4 c 5.0 NaN 5 A NaN 3.0 6 B NaN 4.0 7 C NaN 5.0
data = {'Chinese': [66, 95, 93, 90,80], 'Math': [30, 98, 96, 77, 90], 'English': [65, 85, 92, 88, 90]}
df = DataFrame(data, index=['ZhangFei', 'GuanYu', 'LiuBei', 'DianWei', 'XuChu'], columns=['Chinese', 'Math', 'English'])
print(df)
Chinese Math English ZhangFei 66 30 65 GuanYu 95 98 85 LiuBei 93 96 92 DianWei 90 77 88 XuChu 80 90 90
print(df.loc['ZhangFei'])
Chinese 66 Math 30 English 65 Name: ZhangFei, dtype: int64
print(df.iloc[0])
Chinese 66 Math 30 English 65 Name: ZhangFei, dtype: int64
print(df.columns)
Index(['Chinese', 'Math', 'English'], dtype='object')
print(df.iloc[2]['Math'])
96
print(df.iloc[2]['Chinese'])
93