import numpy as np
import pandas as pd
from pandas import Series, DataFrame

s1 = Series([1,2,3],index=['A','B','C'])
s1

A    1
B    2
C    3
dtype: int64

s2 = Series([4,5,6,7],index=['B','C','D','E'])
s2

B    4
C    5
D    6
E    7
dtype: int64

s1 + s2

A    NaN
B    6.0
C    8.0
D    NaN
E    NaN
dtype: float64

对应index相加，对不上的就是NaN

DataFrame的运算

df_a = DataFrame(np.arange(4).reshape(2,2),index=['A','B'],columns=['北京','上海'])

df_b = DataFrame(np.arange(9).reshape(3,3),index=['A','B','C'],columns=['北京','上海','广州'])

df_a

df_b

df_a + df_b

类似的，index和columns对应的部分可以相加，否则为NaN

df_c = DataFrame([[1,2,3],[4,5,np.nan],[7,8,9]],index=['A','B','C'],columns=['c1','c2','c3'])
df_c

df_c.sum()

c1    12.0
c2    15.0
c3    12.0
dtype: float64

df_c.sum(axis = 1)

A     6.0
B     9.0
C    24.0
dtype: float64

type(df_c.sum())

pandas.core.series.Series

DataFrame中求和的时候会忽略NaN
axis = 1 可以指定行的计算

df_c.describe()

s1.index

Index(['A', 'B', 'C'], dtype='object')

s1.sort_values()#按value升序

A    1
B    2
C    3
dtype: int64

s1.sort_values(ascending=False)#按value降序

C    3
B    2
A    1
dtype: int64

s1.sort_index(ascending=False)#按index降序

C    3
B    2
A    1
dtype: int64

df_d = DataFrame(np.random.randn(35).reshape(7,5),columns=['A','B','C','D','E'])

df_d

df_d.sort_values('A',ascending=False)#按A列降序

df_d.sort_index(ascending=False)#按index降序

所谓课后练习 ——> 将一个已有csv文件中的三列提取出来，按其中一项降序排列，要求用一行完成（真是。。）。

import pandas as pd

csv_file = open("E:/Python3数据科学入门与实战/project/o25mso/homework/movie_metadata.csv","r",encoding="utf-8")
read_csv_in = pd.read_csv(csv_file)

df = read_csv_in[['imdb_score','director_name','movie_title']].sort_values('imdb_score',ascending=False)

df.to_csv('new_imdb.csv')

#pd.read_csv(open("E:/Python3数据科学入门与实战/project/o25mso/homework/movie_metadata.csv"))[['imdb_score','director_name','movie_title']].sort_values('imdb_score',ascending=False)

!ls

csv_practice.ipynb
demo.ipynb
new_imdb.csv
practice-2018-08-11.ipynb

有中文路径时通过open("filename","r",encoding="utf-8")读进来，
再用pandas.read_csv()读。注意用utf-8读取否则会出现问题。
这样做好处主要是允许路径有中文

df.head()