import pandas as pd
df=pd.DataFrame({
"name":["Tom","Jerry","Tony","June"],
"age":[16,42,38,22],
"province":["A","B","C","D"],
"country":["Amerian","Japan","Ruisa","Roman"]
})
print(df)
#获取所有列名
print(df.columns)
#获取所有行名
print(df.index)
#获取所有值
print(df.values)
#查看顶部n行
print(df.head(2))
#查看尾部n行
print(df.tail(2))
#获取所有统计摘要
print(df.describe())
# count 4.000000
# mean 30.000000
# std 12.110601
# min 16.000000
# 25% 22.000000
# 50% 31.000000
# 75% 39.000000
# max 42.000000
#转置数据
print(df.T)
#排序,按index
df1=df.sort_index(ascending=False)
print(df1)
#排序，按value
df2=df.sort_values(by="age",ascending=False)
print(df2)
df3=df.sort_values(by="province",ascending=True)
print(df3)
#获取单列
print(df["country"])
#获取多行
print(df[0:3])
#获取多列
df4=df.loc[:,["country","name"]]
print(df4)
#获取单行
df5=df.loc[2]
print(df5)
#获取多行多列
print(df.loc[[0,2],["country","name"]])
df6=pd.DataFrame({
"name":["PF","QC"],
"age":[16,22],
"province":["A","B"],
"country":["Ruisa","Roman"]
})
print(df6)
#多数据合并concat
df7=pd.concat([df,df6],ignore_index=True)
print(df7)
#去重
res=df7.duplicated("country")
print(res)
res1=df7.drop_duplicates("country")
print(res1)
print("--------------------")
# df7.to_csv("lianxi.csv")
df7.to_excel("lianxi.xlsx",sheet_name="Sname")
"C:\Program Files\Python37\python.exe" C:/Users/Administrator/Desktop/note/exer/lianxi.py
name age province country
0 Tom 16 A Amerian
1 Jerry 42 B Japan
2 Tony 38 C Ruisa
3 June 22 D Roman
Index(['name', 'age', 'province', 'country'], dtype='object')
RangeIndex(start=0, stop=4, step=1)
[['Tom' 16 'A' 'Amerian']
['Jerry' 42 'B' 'Japan']
['Tony' 38 'C' 'Ruisa']
['June' 22 'D' 'Roman']]
name age province country
0 Tom 16 A Amerian
1 Jerry 42 B Japan
name age province country
2 Tony 38 C Ruisa
3 June 22 D Roman
age
count 4.000000
mean 29.500000
std 12.476645
min 16.000000
25% 20.500000
50% 30.000000
75% 39.000000
max 42.000000
0 1 2 3
name Tom Jerry Tony June
age 16 42 38 22
province A B C D
country Amerian Japan Ruisa Roman
name age province country
3 June 22 D Roman
2 Tony 38 C Ruisa
1 Jerry 42 B Japan
0 Tom 16 A Amerian
name age province country
1 Jerry 42 B Japan
2 Tony 38 C Ruisa
3 June 22 D Roman
0 Tom 16 A Amerian
name age province country
0 Tom 16 A Amerian
1 Jerry 42 B Japan
2 Tony 38 C Ruisa
3 June 22 D Roman
0 Amerian
1 Japan
2 Ruisa
3 Roman
Name: country, dtype: object
name age province country
0 Tom 16 A Amerian
1 Jerry 42 B Japan
2 Tony 38 C Ruisa
country name
0 Amerian Tom
1 Japan Jerry
2 Ruisa Tony
3 Roman June
name Tony
age 38
province C
country Ruisa
Name: 2, dtype: object
country name
0 Amerian Tom
2 Ruisa Tony
name age province country
0 PF 16 A Ruisa
1 QC 22 B Roman
name age province country
0 Tom 16 A Amerian
1 Jerry 42 B Japan
2 Tony 38 C Ruisa
3 June 22 D Roman
4 PF 16 A Ruisa
5 QC 22 B Roman
0 False
1 False
2 False
3 False
4 True
5 True
dtype: bool
name age province country
0 Tom 16 A Amerian
1 Jerry 42 B Japan
2 Tony 38 C Ruisa
3 June 22 D Roman
--------------------
Process finished with exit code 0
