简书大神SeanCheney的译作，我作了些格式调整和文章目录结构的变化，更适合自己阅读，以后翻阅是更加方便自己查找吧

import pandas as pd
import numpy as np

设定最大列数和最大行数

pd.set_option('max_columns', 5, 'max_rows', 10)

1 DataFrame的结构

movie = pd.read_csv('data/movie.csv')

movie.shape

(4916, 28)

2 访问DataFrame的组件

2.1 组件获取及其类型

columns = movie.columns

type(columns)

pandas.core.indexes.base.Index

columns

Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',
       'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',
       'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',
       'movie_title', 'num_voted_users', 'cast_total_facebook_likes',
       'actor_3_name', 'facenumber_in_poster', 'plot_keywords',
       'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',
       'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',
       'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],
      dtype='object')

index = movie.index

type(index)

pandas.core.indexes.range.RangeIndex

index

RangeIndex(start=0, stop=4916, step=1)

data = movie.values

type(data)

numpy.ndarray

data

array([['Color', 'James Cameron', 723.0, ..., 7.9, 1.78, 33000],
       ['Color', 'Gore Verbinski', 302.0, ..., 7.1, 2.35, 0],
       ['Color', 'Sam Mendes', 602.0, ..., 6.8, 2.35, 85000],
       ...,
       ['Color', 'Benjamin Roberds', 13.0, ..., 6.3, nan, 16],
       ['Color', 'Daniel Hsia', 14.0, ..., 6.3, 2.35, 660],
       ['Color', 'Jon Gunn', 43.0, ..., 6.6, 1.85, 456]], dtype=object)

2.2 索引类型

判断是不是子类型

issubclass(pd.core.indexes.range.RangeIndex,pd.Index)

True

访问index的值，index的值是个列表，所以可以索引或切片

index.values

array([   0,    1,    2, ..., 4913, 4914, 4915])

3 理解数据类型

movie.dtypes

color                       object
director_name               object
num_critic_for_reviews     float64
duration                   float64
director_facebook_likes    float64
                            ...   
title_year                 float64
actor_2_facebook_likes     float64
imdb_score                 float64
aspect_ratio               float64
movie_facebook_likes         int64
Length: 28, dtype: object

显示各类型的数量

movie.get_dtype_counts()

float64    13
int64       3
object     12
dtype: int64

4 Series 结构

选择一列数据，作为Series

movie['director_name']

0           James Cameron
1          Gore Verbinski
2              Sam Mendes
3       Christopher Nolan
4             Doug Walker
              ...        
4911          Scott Smith
4912                  NaN
4913     Benjamin Roberds
4914          Daniel Hsia
4915             Jon Gunn
Name: director_name, Length: 4916, dtype: object

也可以通过属性的方式选取

movie.director_name

0           James Cameron
1          Gore Verbinski
2              Sam Mendes
3       Christopher Nolan
4             Doug Walker
              ...        
4911          Scott Smith
4912                  NaN
4913     Benjamin Roberds
4914          Daniel Hsia
4915             Jon Gunn
Name: director_name, Length: 4916, dtype: object

type(movie['director_name'])

pandas.core.series.Series

4.1 调用Series方法

查看Series所有不重复的指令

s_attr_methods = set(dir(pd.Series))

len(s_attr_methods)

查看DataFrame所有不重复的指令

df_attr_methods = set(dir(pd.DataFrame))

len(df_attr_methods)

这两个集合中有多少共有的指令

len(s_attr_methods & df_attr_methods)

4.2 Series基础方法

选取director和actor_1_fb_likes两列

director = movie['director_name']
actor_1_fb_likes  = movie['actor_1_facebook_likes']

查看series头部信息

director.head()

0        James Cameron
1       Gore Verbinski
2           Sam Mendes
3    Christopher Nolan
4          Doug Walker
Name: director_name, dtype: object

统计series值出现的频数

director.value_counts()

Steven Spielberg    26
Woody Allen         22
Clint Eastwood      20
Martin Scorsese     20
Spike Lee           16
                    ..
John Duigan          1
Ray Griggs           1
Lena Dunham          1
Dario Argento        1
Eric Mendelsohn      1
Name: director_name, Length: 2397, dtype: int64

统计series值出现的频率

director.value_counts(normalize=True)

Steven Spielberg    0.005401
Woody Allen         0.004570
Clint Eastwood      0.004155
Martin Scorsese     0.004155
Spike Lee           0.003324
                      ...   
John Duigan         0.000208
Ray Griggs          0.000208
Lena Dunham         0.000208
Dario Argento       0.000208
Eric Mendelsohn     0.000208
Name: director_name, Length: 2397, dtype: float64

长度相关

len(director)

director.size

director.shape

(4916,)

director有多少非空值

director.count()

空值个数(会有更加直接的方法)

director.size - director.count()

4.3 Series统计信息

最小值、最大值、平均值、中位数、标准差、总和

actor_1_fb_likes.min(), actor_1_fb_likes.max()

(0.0, 640000.0)

actor_1_fb_likes.mean(), actor_1_fb_likes.median()

(6494.488490527602, 982.0)

actor_1_fb_likes.std(), actor_1_fb_likes.sum()

(15106.986883848309, 31881444.0)

数值描述信息

actor_1_fb_likes.describe()

count      4909.000000
mean       6494.488491
std       15106.986884
min           0.000000
25%         607.000000
50%         982.000000
75%       11000.000000
max      640000.000000
Name: actor_1_facebook_likes, dtype: float64

字符描述信息

director.describe()

count                 4814
unique                2397
top       Steven Spielberg
freq                    26
Name: director_name, dtype: object

任意分为点

actor_1_fb_likes.quantile(.2)

510.0

actor_1_fb_likes.quantile([.1, .2, .3, .4, .5, .6, .7, .8, .9])

0.1      240.0
0.2      510.0
0.3      694.0
0.4      854.0
0.5      982.0
0.6     1000.0
0.7     8000.0
0.8    13000.0
0.9    18000.0
Name: actor_1_facebook_likes, dtype: float64

4.4 空值处理

判断是否有缺失值

actor_1_fb_likes.hasnans

True

缺失值的个数

actor_1_fb_likes.isnull().sum()

选取缺失值

actor_1_fb_likes[actor_1_fb_likes.isnull()]

4401   NaN
4418   NaN
4608   NaN
4721   NaN
4822   NaN
4823   NaN
4864   NaN
Name: actor_1_facebook_likes, dtype: float64

非空值

actor_1_fb_likes.isnull()

0       False
1       False
2       False
3       False
4       False
        ...  
4911    False
4912    False
4913    False
4914    False
4915    False
Name: actor_1_facebook_likes, Length: 4916, dtype: bool

bool_sig = actor_1_fb_likes.notnull()

判断所有的bool是否都为true

bool_sig.all()

False

填充缺失值

actor_1_fb_likes.count()

actor_1_fb_likes_filled = actor_1_fb_likes.fillna(0)

actor_1_fb_likes_filled.count()

删除缺失值

actor_1_fb_likes_dropped = actor_1_fb_likes.dropna()

actor_1_fb_likes_dropped.size

4.5 在Series上使用运算符

imdb_score = movie['imdb_score']

加减乘除

imdb_score + 1

0       8.9
1       8.1
2       7.8
3       9.5
4       8.1
       ... 
4911    8.7
4912    8.5
4913    7.3
4914    7.3
4915    7.6
Name: imdb_score, Length: 4916, dtype: float64

函数实现

imdb_score.add(1)

0       8.9
1       8.1
2       7.8
3       9.5
4       8.1
       ... 
4911    8.7
4912    8.5
4913    7.3
4914    7.3
4915    7.6
Name: imdb_score, Length: 4916, dtype: float64

4.6 类型转化

imdb_score.dtype

dtype('float64')

imdb_score = imdb_score.astype(int)

imdb_score.dtype

dtype('int64')

5 使dataframe索引有意义

movie.shape

(4916, 28)

movie.tail()

	color	director_name	...	aspect_ratio	movie_facebook_likes
4911	Color	Scott Smith	...	NaN	84
4912	Color	NaN	...	16.00	32000
4913	Color	Benjamin Roberds	...	NaN	16
4914	Color	Daniel Hsia	...	2.35	660
4915	Color	Jon Gunn	...	1.85	456

5 rows × 28 columns

5.1 给索引重命名

movie.index.name = 'row_index'

movie.columns.name = 'col_index'

movie.tail()

col_index	color	director_name	...	aspect_ratio	movie_facebook_likes
row_index
4911	Color	Scott Smith	...	NaN	84
4912	Color	NaN	...	16.00	32000
4913	Color	Benjamin Roberds	...	NaN	16
4914	Color	Daniel Hsia	...	2.35	660
4915	Color	Jon Gunn	...	1.85	456

5 rows × 28 columns

5.2 重设索引

将dataframe中存在某列或多列作为索引

movie2 = movie.set_index('movie_title')

movie2.tail()

col_index	color	director_name	...	aspect_ratio	movie_facebook_likes
movie_title
Signed Sealed Delivered	Color	Scott Smith	...	NaN	84
The Following	Color	NaN	...	16.00	32000
A Plague So Pleasant	Color	Benjamin Roberds	...	NaN	16
Shanghai Calling	Color	Daniel Hsia	...	2.35	660
My Date with Drew	Color	Jon Gunn	...	1.85	456

5 rows × 27 columns

另一种方式

movie = pd.read_csv('data/movie.csv',index_col = 'movie_title')

还原为默认整数索引

movie2.reset_index().tail()

col_index	movie_title	color	...	aspect_ratio	movie_facebook_likes
4911	Signed Sealed Delivered	Color	...	NaN	84
4912	The Following	Color	...	16.00	32000
4913	A Plague So Pleasant	Color	...	NaN	16
4914	Shanghai Calling	Color	...	2.35	660
4915	My Date with Drew	Color	...	1.85	456

5 rows × 28 columns

6 重命名行名和列名

通过rename()重命名

idx_rename = {'Avatar':'Ratava', 'Spectre': 'Ertceps'}

col_rename = {'director_name':'Director Name','num_critic_for_reviews': 'Critical Reviews'}

movie.rename(index=idx_rename, columns=col_rename).head()

	color	Director Name	...	aspect_ratio	movie_facebook_likes
movie_title
Ratava	Color	James Cameron	...	1.78	33000
Pirates of the Caribbean: At World's End	Color	Gore Verbinski	...	2.35	0
Ertceps	Color	Sam Mendes	...	2.35	85000
The Dark Knight Rises	Color	Christopher Nolan	...	2.35	164000
Star Wars: Episode VII - The Force Awakens	NaN	Doug Walker	...	NaN	0

5 rows × 27 columns

列表的方式

index = movie.index
columns = movie.columns

index_list = index.tolist()
column_list = columns.tolist()

index_list[0] = 'Ratava'
index_list[2] = 'Ertceps'
column_list[1] = 'Director Name'
column_list[2] = 'Critical Reviews'

movie.index = index_list
movie.columns = column_list

movie.head()

	color	Director Name	...	aspect_ratio	movie_facebook_likes
Ratava	Color	James Cameron	...	1.78	33000
Pirates of the Caribbean: At World's End	Color	Gore Verbinski	...	2.35	0
Ertceps	Color	Sam Mendes	...	2.35	85000
The Dark Knight Rises	Color	Christopher Nolan	...	2.35	164000
Star Wars: Episode VII - The Force Awakens	NaN	Doug Walker	...	NaN	0

5 rows × 27 columns

7 创建、删除列

通过[列名]添加新列

movie = pd.read_csv('data/movie.csv')

movie['has_seen'] = 0

movie['actor_director_facebook_likes'] = (movie['actor_1_facebook_likes'] + movie['actor_2_facebook_likes'])

movie.shape,movie['actor_director_facebook_likes'].shape

((4916, 30), (4916,))

删除行/列

movie.drop(['actor_director_facebook_likes','actor_1_facebook_likes'],axis=1)

	color	director_name	...	movie_facebook_likes	has_seen
0	Color	James Cameron	...	33000	0
1	Color	Gore Verbinski	...	0	0
2	Color	Sam Mendes	...	85000	0
3	Color	Christopher Nolan	...	164000	0
4	NaN	Doug Walker	...	0	0
...	...	...	...	...	...
4911	Color	Scott Smith	...	84	0
4912	Color	NaN	...	32000	0
4913	Color	Benjamin Roberds	...	16	0
4914	Color	Daniel Hsia	...	660	0
4915	Color	Jon Gunn	...	456	0

4916 rows × 28 columns

movie.drop([0,2])

	color	director_name	...	has_seen	actor_director_facebook_likes
1	Color	Gore Verbinski	...	0	45000.0
3	Color	Christopher Nolan	...	0	50000.0
4	NaN	Doug Walker	...	0	143.0
5	Color	Andrew Stanton	...	0	1272.0
6	Color	Sam Raimi	...	0	35000.0
...	...	...	...	...	...
4911	Color	Scott Smith	...	0	1107.0
4912	Color	NaN	...	0	1434.0
4913	Color	Benjamin Roberds	...	0	0.0
4914	Color	Daniel Hsia	...	0	1665.0
4915	Color	Jon Gunn	...	0	109.0

4914 rows × 30 columns

来源：https://www.cnblogs.com/shiyushiyu/p/9712998.html

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Pandas CookBook -- 01Pandas基础

1 DataFrame的结构

2 访问DataFrame的组件

2.1 组件获取及其类型

2.2 索引类型

3 理解数据类型

4 Series 结构

4.1 调用Series方法

4.2 Series基础方法

4.3 Series统计信息

4.4 空值处理

4.5 在Series上使用运算符

4.6 类型转化

5 使dataframe索引有意义

5.1 给索引重命名

5.2 重设索引

6 重命名行名和列名

7 创建、删除列