pandas-20 DataFrame()的基本操作

pandas-20 DataFrame()的基本操作

感觉上pandas的DataFrame就像numpy中的矩阵，不过它拥有列名和索引名，实际操作起来会更方便一些。

如：

df = pd.read_clipboard()

df.columns

df.Ratings

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# 使用 浏览器 打开 某网址
#import webbrowser
#link = 'https://www.tiobe.com/tiobe-index'
#webbrowser.open(link)
# 从 粘贴板上 复制
df = pd.read_clipboard()
print(df)
'''
   Oct 2018  Oct 2017  Change Programming Language  Ratings Change.1
0         1         1     NaN                 Java  17.801%   +5.37%
1         2         2     NaN                    C  15.376%   +7.00%
2         3         3     NaN                  C++   7.593%   +2.59%
3         4         5  change               Python   7.156%   +3.35%
4         5         8  change    Visual Basic .NET   5.884%   +3.15%
'''
print(type(df)) # <class 'pandas.core.frame.DataFrame'>
# 打印出所有的 列名
print(df.columns)
'''
Index(['Oct 2018', 'Oct 2017', 'Change', 'Programming Language', 'Ratings',
       'Change.1'],
      dtype='object')
'''
# 打印出 其中 一列的 值
print(df.Ratings)
'''
0    17.801%
1    15.376%
2     7.593%
3     7.156%
4     5.884%
Name: Ratings, dtype: object
'''
# 可以使用访问字典的方式 访问
print(df['Ratings'])
'''
0    17.801%
1    15.376%
2     7.593%
3     7.156%
4     5.884%
Name: Ratings, dtype: object
'''
print(type(df['Ratings'])) # 每一列 其实都是一个 series ： <class 'pandas.core.series.Series'>
# 拿出其中的某一些列，组成一个新的dataframe
df_new = DataFrame(df, columns=['Change', 'Ratings'])
print(df_new)
df_new = DataFrame(df, columns=['Change', 'Ratings', 'name'])
print(df_new)
'''
   Change Ratings  name
0    Java    None   NaN
1       C    None   NaN
2     C++    None   NaN
3  Python    None   NaN
4  Visual  5.884%   NaN
添加的新列，会默认一 NaN填充
'''
# 为 新列 赋值
df_new['name'] = range(0, 5)
print(df_new)
'''
   Change Ratings  name
0    Java    None     0
1       C    None     1
2     C++    None     2
3  Python    None     3
4  Visual  5.884%     4
'''
# 更改某一列 的值
df_new['Change'] = np.arange(0, 5)
print(df_new)
'''
   Change Ratings  name
0       0    None     0
1       1    None     1
2       2    None     2
3       3    None     3
4       4  5.884%     4
'''
# 直接赋值一个 Series 也可以
df_new['Ratings'] = pd.Series(np.arange(0, 5))
print(df_new)
# 单独为 某列的某几个值赋值
df_new['name'] = pd.Series([100, 300], index = [1, 2])
print(df_new)
'''
   Change  Ratings   name
0       0        0    NaN
1       1        1  100.0
2       2        2  300.0
3       3        3    NaN
4       4        4    NaN
'''