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数据科学和人工智能技术笔记 十九、数据整理(下)

飞龙 1月1日 发布于人工智能 github.com

数据科学和人工智能技术笔记 十九、数据整理(下)

数据科学和人工智能技术笔记 十九、数据整理(下)

1月1日 发布,来源:github.com

十九、数据整理(下)

作者:Chris Albon

译者:飞龙

协议:CC BY-NC-SA 4.0

连接和合并数据帧

# 导入模块
import pandas as pd
from IPython.display import display
from IPython.display import Image

raw_data = {
        'subject_id': ['1', '2', '3', '4', '5'],
        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], 
        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}
df_a = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name'])
df_a
subject_id first_name last_name
0 1 Alex Anderson
1 2 Amy Ackerman
2 3 Allen Ali
3 4 Alice Aoni
4 5 Ayoung Atiches
# 创建第二个数据帧
raw_data = {
        'subject_id': ['4', '5', '6', '7', '8'],
        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], 
        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}
df_b = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name'])
df_b
subject_id first_name last_name
0 4 Billy Bonder
1 5 Brian Black
2 6 Bran Balwner
3 7 Bryce Brice
4 8 Betty Btisan
# 创建第三个数据帧
raw_data = {
        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],
        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}
df_n = pd.DataFrame(raw_data, columns = ['subject_id','test_id'])
df_n
subject_id test_id
0 1 51
1 2 15
2 3 15
3 4 61
4 5 16
5 7 14
6 8 15
7 9 1
8 10 61
9 11 16
# 将两个数据帧按行连接
df_new = pd.concat([df_a, df_b])
df_new
subject_id first_name last_name
0 1 Alex Anderson
1 2 Amy Ackerman
2 3 Allen Ali
3 4 Alice Aoni
4 5 Ayoung Atiches
0 4 Billy Bonder
1 5 Brian Black
2 6 Bran Balwner
3 7 Bryce Brice
4 8 Betty Btisan
# 将两个数据帧按列连接
pd.concat([df_a, df_b], axis=1)
subject_id first_name last_name subject_id first_name last_name
0 1 Alex Anderson 4 Billy Bonder
1 2 Amy Ackerman 5 Brian Black
2 3 Allen Ali 6 Bran Balwner
3 4 Alice Aoni 7 Bryce Brice
4 5 Ayoung Atiches 8 Betty Btisan
# 按两个数据帧按 subject_id 连接
pd.merge(df_new, df_n, on='subject_id')
subject_id first_name last_name test_id
0 1 Alex Anderson 51
1 2 Amy Ackerman 15
2 3 Allen Ali 15
3 4 Alice Aoni 61
4 4 Billy Bonder 61
5 5 Ayoung Atiches 16
6 5 Brian Black 16
7 7 Bryce Brice 14
8 8 Betty Btisan 15
# 将两个数据帧按照左和右数据帧的 subject_id 连接
pd.merge(df_new, df_n, left_on='subject_id', right_on='subject_id')
subject_id first_name last_name test_id
0 1 Alex Anderson 51
1 2 Amy Ackerman 15
2 3 Allen Ali 15
3 4 Alice Aoni 61
4 4 Billy Bonder 61
5 5 Ayoung Atiches 16
6 5 Brian Black 16
7 7 Bryce Brice 14
8 8 Betty Btisan 15

使用外连接来合并。

“全外连接产生表 A 和表 B 中所有记录的集合,带有来自两侧的匹配记录。如果没有匹配,则缺少的一侧将包含空值。” -- [来源](http://blog .codinghorror.com/a-visual-explanation-of-sql-joins/)

pd.merge(df_a, df_b, on='subject_id', how='outer')
subject_id first_name_x last_name_x first_name_y last_name_y
0 1 Alex Anderson NaN NaN
1 2 Amy Ackerman NaN NaN
2 3 Allen Ali NaN NaN
3 4 Alice Aoni Billy Bonder
4 5 Ayoung Atiches Brian Black
5 6 NaN NaN Bran Balwner
6 7 NaN NaN Bryce Brice
7 8 NaN NaN Betty Btisan

使用内连接来合并。

“内联接只生成匹配表 A 和表 B 的记录集。” -- 来源

pd.merge(df_a, df_b, on='subject_id', how='inner')
subject_id first_name_x last_name_x first_name_y last_name_y
0 4 Alice Aoni Billy Bonder
1 5 Ayoung Atiches Brian Black
# 使用右连接来合并
pd.merge(df_a, df_b, on='subject_id', how='right')
subject_id first_name_x last_name_x first_name_y last_name_y
0 4 Alice Aoni Billy Bonder
1 5 Ayoung Atiches Brian Black
2 6 NaN NaN Bran Balwner
3 7 NaN NaN Bryce Brice
4 8 NaN NaN Betty Btisan

使用左连接来合并。

“左外连接从表 A 中生成一组完整的记录,它们在表 B 中有匹配的记录。如果没有匹配,右侧将包含空。” -- 来源

pd.merge(df_a, df_b, on='subject_id', how='left')
subject_id first_name_x last_name_x first_name_y last_name_y
0 1 Alex Anderson NaN NaN
1 2 Amy Ackerman NaN NaN
2 3 Allen Ali NaN NaN
3 4 Alice Aoni Billy Bonder
4 5 Ayoung Atiches Brian Black
# 合并时添加后缀以复制列名称
pd.merge(df_a, df_b, on='subject_id', how='left', suffixes=('_left', '_right'))
subject_id first_name_left last_name_left first_name_right last_name_right
0 1 Alex Anderson NaN NaN
1 2 Amy Ackerman NaN NaN
2 3 Allen Ali NaN NaN
3 4 Alice Aoni Billy Bonder
4 5 Ayoung Atiches Brian Black
# 基于索引的合并
pd.merge(df_a, df_b, right_index=True, left_index=True)
subject_id_x first_name_x last_name_x subject_id_y first_name_y last_name_y
0 1 Alex Anderson 4 Billy Bonder
1 2 Amy Ackerman 5 Brian Black
2 3 Allen Ali 6 Bran Balwner
3 4 Alice Aoni 7 Bryce Brice
4 5 Ayoung Atiches 8 Betty Btisan

列出 pandas 列中的唯一值

特别感谢 Bob Haffner 指出了一种更好的方法。

# 导入模块
import pandas as pd

# 设置 ipython 的最大行显示
pd.set_option('display.max_row', 1000)

# 设置 ipython 的最大列宽
pd.set_option('display.max_columns', 50)

# 创建示例数据帧
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014
# 列出 df['name'] 的唯一值
df.name.unique()

# array(['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], dtype=object) 

加载 JSON 文件

# 加载库
import pandas as pd

# 创建 JSON 文件的 URL(或者可以是文件路径)
url = 'https://raw.githubusercontent.com/chrisalbon/simulated_datasets/master/data.json'

# 将 JSON 文件加载到数据框中
df = pd.read_json(url, orient='columns')

# 查看前十行
df.head(10)
category datetime integer
0 0 2015-01-01 00:00:00 5
1 0 2015-01-01 00:00:01 5
10 0 2015-01-01 00:00:10 5
11 0 2015-01-01 00:00:11 5
12 0 2015-01-01 00:00:12 8
13 0 2015-01-01 00:00:13 9
14 0 2015-01-01 00:00:14 8
15 0 2015-01-01 00:00:15 8
16 0 2015-01-01 00:00:16 2
17 0 2015-01-01 00:00:17 1

加载 Excel 文件

# 加载库
import pandas as pd

# 创建 Excel 文件的 URL(或者可以是文件路径)
url = 'https://raw.githubusercontent.com/chrisalbon/simulated_datasets/master/data.xlsx'

# 将 Excel 文件的第一页加载到数据框中
df = pd.read_excel(url, sheetname=0, header=1)

# 查看前十行
df.head(10)
5 2015-01-01 00:00:00 0
0 5 2015-01-01 00:00:01 0
1 9 2015-01-01 00:00:02 0
2 6 2015-01-01 00:00:03 0
3 6 2015-01-01 00:00:04 0
4 9 2015-01-01 00:00:05 0
5 7 2015-01-01 00:00:06 0
6 1 2015-01-01 00:00:07 0
7 6 2015-01-01 00:00:08 0
8 9 2015-01-01 00:00:09 0
9 5 2015-01-01 00:00:10 0

将 Excel 表格加载为数据帧

# 导入模块
import pandas as pd

# 加载 excel 文件并赋给 xls_file
xls_file = pd.ExcelFile('../data/example.xls')
xls_file

# <pandas.io.excel.ExcelFile at 0x111912be0> 

# 查看电子表格的名称
xls_file.sheet_names

# ['Sheet1'] 

# 将 xls 文件 的 Sheet1 加载为数据帧
df = xls_file.parse('Sheet1')
df
year deaths_attacker deaths_defender soldiers_attacker soldiers_defender wounded_attacker wounded_defender
0 1945 425 423 2532 37235 41 14
1 1956 242 264 6346 2523 214 1424
2 1964 323 1231 3341 2133 131 131
3 1969 223 23 6732 1245 12 12
4 1971 783 23 12563 2671 123 34
5 1981 436 42 2356 7832 124 124
6 1982 324 124 253 2622 264 1124
7 1992 3321 631 5277 3331 311 1431
8 1999 262 232 2732 2522 132 122
9 2004 843 213 6278 26773 623 2563

加载 CSV

# 导入模块
import pandas as pd
import numpy as np

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', ".", 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, ".", "."],
        'postTestScore': ["25,000", "94,000", 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df
first_name last_name age preTestScore postTestScore
0 Jason Miller 42 4 25,000
1 Molly Jacobson 52 24 94,000
2 Tina . 36 31 57
3 Jake Milner 24 . 62
4 Amy Cooze 73 . 70
# 将数据帧保存为工作目录中的 csv
df.to_csv('pandas_dataframe_importing_csv/example.csv')

df = pd.read_csv('pandas_dataframe_importing_csv/example.csv')
df
Unnamed: 0 first_name last_name age preTestScore postTestScore
0 0 Jason Miller 42 4 25,000
1 1 Molly Jacobson 52 24 94,000
2 2 Tina . 36 31 57
3 3 Jake Milner 24 . 62
4 4 Amy Cooze 73 . 70
# 加载无头 CSV
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', header=None)
df
0 1 2 3 4 5
0 NaN first_name last_name age preTestScore postTestScore
1 0.0 Jason Miller 42 4 25,000
2 1.0 Molly Jacobson 52 24 94,000
3 2.0 Tina . 36 31 57
4 3.0 Jake Milner 24 . 62
5 4.0 Amy Cooze 73 . 70
# 在加载 csv 时指定列名称
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', names=['UID', 'First Name', 'Last Name', 'Age', 'Pre-Test Score', 'Post-Test Score'])
df
UID First Name Last Name Age Pre-Test Score Post-Test Score
0 NaN first_name last_name age preTestScore postTestScore
1 0.0 Jason Miller 42 4 25,000
2 1.0 Molly Jacobson 52 24 94,000
3 2.0 Tina . 36 31 57
4 3.0 Jake Milner 24 . 62
5 4.0 Amy Cooze 73 . 70
# 通过将索引列设置为 UID 来加载 csv
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', index_col='UID', names=['UID', 'First Name', 'Last Name', 'Age', 'Pre-Test Score', 'Post-Test Score'])
df
First Name Last Name Age Pre-Test Score Post-Test Score
UID
NaN first_name last_name age preTestScore postTestScore
0.0 Jason Miller 42 4 25,000
1.0 Molly Jacobson 52 24 94,000
2.0 Tina . 36 31 57
3.0 Jake Milner 24 . 62
4.0 Amy Cooze 73 . 70
# 在加载 csv 时将索引列设置为名字和姓氏
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', index_col=['First Name', 'Last Name'], names=['UID', 'First Name', 'Last Name', 'Age', 'Pre-Test Score', 'Post-Test Score'])
df
UID Age Pre-Test Score Post-Test Score
First Name Last Name
first_name last_name NaN age preTestScore postTestScore
Jason Miller 0.0 42 4 25,000
Molly Jacobson 1.0 52 24 94,000
Tina . 2.0 36 31 57
Jake Milner 3.0 24 . 62
Amy Cooze 4.0 73 . 70
# 在加载 csv 时指定 '.' 为缺失值
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', na_values=['.'])
pd.isnull(df)
Unnamed: 0 first_name last_name age preTestScore postTestScore
0 False False False False False False
1 False False False False False False
2 False False True False False False
3 False False False False True False
4 False False False False True False
# 加载csv,同时指定 '.' 和 'NA' 为“姓氏”列的缺失值,指定 '.' 为 preTestScore 列的缺失值
sentinels = {'Last Name': ['.', 'NA'], 'Pre-Test Score': ['.']}

df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', na_values=sentinels)
df
Unnamed: 0 first_name last_name age preTestScore postTestScore
0 0 Jason Miller 42 4 25,000
1 1 Molly Jacobson 52 24 94,000
2 2 Tina . 36 31 57
3 3 Jake Milner 24 . 62
4 4 Amy Cooze 73 . 70
# 在加载 csv 时跳过前 3 行
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', na_values=sentinels, skiprows=3)
df
2 Tina . 36 31 57
0 3 Jake Milner 24 . 62
1 4 Amy Cooze 73 . 70
# 加载 csv,同时将数字字符串中的 ',' 解释为千位分隔符
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', thousands=',')
df
Unnamed: 0 first_name last_name age preTestScore postTestScore
0 0 Jason Miller 42 4 25000
1 1 Molly Jacobson 52 24 94000
2 2 Tina . 36 31 57
3 3 Jake Milner 24 . 62
4 4 Amy Cooze 73 . 70

长到宽的格式

# 导入模块
import pandas as pd

raw_data = {'patient': [1, 1, 1, 2, 2], 
        'obs': [1, 2, 3, 1, 2], 
        'treatment': [0, 1, 0, 1, 0],
        'score': [6252, 24243, 2345, 2342, 23525]} 
df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])
df
patient obs treatment score
0 1 1 0 6252
1 1 2 1 24243
2 1 3 0 2345
3 2 1 1 2342
4 2 2 0 23525

制作“宽的”数据。

现在,我们将创建一个“宽的”数据帧,其中行数按患者编号,列按观测编号,单元格值为得分值。

df.pivot(index='patient', columns='obs', values='score')
obs 1 2 3
patient
1 6252.0 24243.0 2345.0
2 2342.0 23525.0 NaN

在数据帧中小写列名

# 导入模块
import pandas as pd

# 设置 ipython 的最大行显示
pd.set_option('display.max_row', 1000)

# 设置 ipython 的最大列宽
pd.set_option('display.max_columns', 50)

# 创建示例数据帧
data = {'NAME': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'YEAR': [2012, 2012, 2013, 2014, 2014], 
        'REPORTS': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
NAME REPORTS YEAR
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014
# 小写列名称
# Map the lowering function to all column names
df.columns = map(str.lower, df.columns)

df
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014

使用函数创建新列

# 导入模块
import pandas as pd

# 示例数据帧
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'name', 'preTestScore', 'postTestScore'])
df
regiment company name preTestScore postTestScore
0 Nighthawks 1st Miller 4 25
1 Nighthawks 1st Jacobson 24 94
2 Nighthawks 2nd Ali 31 57
3 Nighthawks 2nd Milner 2 62
4 Dragoons 1st Cooze 3 70
5 Dragoons 1st Jacon 4 25
6 Dragoons 2nd Ryaner 24 94
7 Dragoons 2nd Sone 31 57
8 Scouts 1st Sloan 2 62
9 Scouts 1st Piger 3 70
10 Scouts 2nd Riani 2 62
11 Scouts 2nd Ali 3 70
# 创建一个接受两个输入,pre 和 post 的函数
def pre_post_difference(pre, post):
    # 返回二者的差
    return post - pre

# 创建一个变量,它是函数的输出
df['score_change'] = pre_post_difference(df['preTestScore'], df['postTestScore'])

# 查看数据帧
df
regiment company name preTestScore postTestScore score_change
0 Nighthawks 1st Miller 4 25 21
1 Nighthawks 1st Jacobson 24 94 70
2 Nighthawks 2nd Ali 31 57 26
3 Nighthawks 2nd Milner 2 62 60
4 Dragoons 1st Cooze 3 70 67
5 Dragoons 1st Jacon 4 25 21
6 Dragoons 2nd Ryaner 24 94 70
7 Dragoons 2nd Sone 31 57 26
8 Scouts 1st Sloan 2 62 60
9 Scouts 1st Piger 3 70 67
10 Scouts 2nd Riani 2 62 60
11 Scouts 2nd Ali 3 70 67
# 创建一个接受一个输入 x 的函数
def score_multipler_2x_and_3x(x):
    # 返回两个东西,2x 和 3x
    return x*2, x*3

# 创建两个新变量,它是函数的两个输出
df['post_score_x2'], df['post_score_x3'] = zip(*df['postTestScore'].map(score_multipler_2x_and_3x))
df
regiment company name preTestScore postTestScore score_change post_score_x2 post_score_x3
0 Nighthawks 1st Miller 4 25 21 50 75
1 Nighthawks 1st Jacobson 24 94 70 188 282
2 Nighthawks 2nd Ali 31 57 26 114 171
3 Nighthawks 2nd Milner 2 62 60 124 186
4 Dragoons 1st Cooze 3 70 67 140 210
5 Dragoons 1st Jacon 4 25 21 50 75
6 Dragoons 2nd Ryaner 24 94 70 188 282
7 Dragoons 2nd Sone 31 57 26 114 171
8 Scouts 1st Sloan 2 62 60 124 186
9 Scouts 1st Piger 3 70 67 140 210
10 Scouts 2nd Riani 2 62 60 124 186
11 Scouts 2nd Ali 3 70 67 140 210

将外部值映射为数据帧的值

# 导入模块
import pandas as pd

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'city': ['San Francisco', 'Baltimore', 'Miami', 'Douglas', 'Boston']}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'city'])
df
first_name last_name age city
0 Jason Miller 42 San Francisco
1 Molly Jacobson 52 Baltimore
2 Tina Ali 36 Miami
3 Jake Milner 24 Douglas
4 Amy Cooze 73 Boston
# 创建值的字典
city_to_state = { 'San Francisco' : 'California', 
                  'Baltimore' : 'Maryland', 
                  'Miami' : 'Florida', 
                  'Douglas' : 'Arizona', 
                  'Boston' : 'Massachusetts'}

df['state'] = df['city'].map(city_to_state)
df
first_name last_name age city state
0 Jason Miller 42 San Francisco California
1 Molly Jacobson 52 Baltimore Maryland
2 Tina Ali 36 Miami Florida
3 Jake Milner 24 Douglas Arizona
4 Amy Cooze 73 Boston Massachusetts

数据帧中的缺失数据

# 导入模块
import pandas as pd
import numpy as np

raw_data = {'first_name': ['Jason', np.nan, 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', np.nan, 'Ali', 'Milner', 'Cooze'], 
        'age': [42, np.nan, 36, 24, 73], 
        'sex': ['m', np.nan, 'f', 'm', 'f'], 
        'preTestScore': [4, np.nan, np.nan, 2, 3],
        'postTestScore': [25, np.nan, np.nan, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'sex', 'preTestScore', 'postTestScore'])
df
first_name last_name age sex preTestScore postTestScore
0 Jason Miller 42.0 m 4.0 25.0
1 NaN NaN NaN NaN NaN NaN
2 Tina Ali 36.0 f NaN NaN
3 Jake Milner 24.0 m 2.0 62.0
4 Amy Cooze 73.0 f 3.0 70.0
# 丢弃缺失值
df_no_missing = df.dropna()
df_no_missing
first_name last_name age sex preTestScore postTestScore
0 Jason Miller 42.0 m 4.0 25.0
3 Jake Milner 24.0 m 2.0 62.0
4 Amy Cooze 73.0 f 3.0 70.0

# 删除所有单元格为 NA 的行
df_cleaned = df.dropna(how='all')
df_cleaned
first_name last_name age sex preTestScore postTestScore
0 Jason Miller 42.0 m 4.0 25.0
2 Tina Ali 36.0 f NaN NaN
3 Jake Milner 24.0 m 2.0 62.0
4 Amy Cooze 73.0 f 3.0 70.0
# 创建一个缺失值填充的新列
df['location'] = np.nan
df
first_name last_name age sex preTestScore postTestScore location
0 Jason Miller 42.0 m 4.0 25.0 NaN
1 NaN NaN NaN NaN NaN NaN NaN
2 Tina Ali 36.0 f NaN NaN NaN
3 Jake Milner 24.0 m 2.0 62.0 NaN
4 Amy Cooze 73.0 f 3.0 70.0 NaN
# 如果列仅包含缺失值,删除列
df.dropna(axis=1, how='all')
first_name last_name age sex preTestScore postTestScore
0 Jason Miller 42.0 m 4.0 25.0
1 NaN NaN NaN NaN NaN NaN
2 Tina Ali 36.0 f NaN NaN
3 Jake Milner 24.0 m 2.0 62.0
4 Amy Cooze 73.0 f 3.0 70.0
# 删除少于五个观测值的行
# 这对时间序列来说非常有用
df.dropna(thresh=5)
first_name last_name age sex preTestScore postTestScore location
0 Jason Miller 42.0 m 4.0 25.0 NaN
3 Jake Milner 24.0 m 2.0 62.0 NaN
4 Amy Cooze 73.0 f 3.0 70.0 NaN
# 用零填充缺失数据
df.fillna(0)
first_name last_name age sex preTestScore postTestScore location
0 Jason Miller 42.0 m 4.0 25.0 0.0
1 0 0 0.0 0 0.0 0.0 0.0
2 Tina Ali 36.0 f 0.0 0.0 0.0
3 Jake Milner 24.0 m 2.0 62.0 0.0
4 Amy Cooze 73.0 f 3.0 70.0 0.0
# 使用 preTestScore 的平均值填充 preTestScore 中的缺失
# inplace=True 表示更改会立即保存到 df 中
df["preTestScore"].fillna(df["preTestScore"].mean(), inplace=True)
df
first_name last_name age sex preTestScore postTestScore location
0 Jason Miller 42.0 m 4.0 25.0 NaN
1 NaN NaN NaN NaN 3.0 NaN NaN
2 Tina Ali 36.0 f 3.0 NaN NaN
3 Jake Milner 24.0 m 2.0 62.0 NaN
4 Amy Cooze 73.0 f 3.0 70.0 NaN

# 使用 postTestScore 的每个性别的均值填充 postTestScore 中的缺失
df["postTestScore"].fillna(df.groupby("sex")["postTestScore"].transform("mean"), inplace=True)
df
first_name last_name age sex preTestScore postTestScore location
0 Jason Miller 42.0 m 4.0 25.0 NaN
1 NaN NaN NaN NaN 3.0 NaN NaN
2 Tina Ali 36.0 f 3.0 70.0 NaN
3 Jake Milner 24.0 m 2.0 62.0 NaN
4 Amy Cooze 73.0 f 3.0 70.0 NaN
# 选择年龄不是 NaN 且性别不是 NaN 的行
df[df['age'].notnull() & df['sex'].notnull()]
first_name last_name age sex preTestScore postTestScore location
0 Jason Miller 42.0 m 4.0 25.0 NaN
2 Tina Ali 36.0 f 3.0 70.0 NaN
3 Jake Milner 24.0 m 2.0 62.0 NaN
4 Amy Cooze 73.0 f 3.0 70.0 NaN

pandas 中的移动平均

# 导入模块
import pandas as pd

# 创建数据
data = {'score': [1,1,1,2,2,2,3,3,3]}

# 创建数据帧
df = pd.DataFrame(data)

# 查看数据帧
df
score
0 1
1 1
2 1
3 2
4 2
5 2
6 3
7 3
8 3
# 计算移动平均。也就是说,取前两个值,取平均值
# 然后丢弃第一个,再加上第三个,以此类推。
df.rolling(window=2).mean()
score
0 NaN
1 1.0
2 1.0
3 1.5
4 2.0
5 2.0
6 2.5
7 3.0
8 3.0

规范化一列

# 导入所需模块
import pandas as pd
from sklearn import preprocessing

# 设置图表为内联
%matplotlib inline

# 创建示例数据帧,带有未规范化的一列
data = {'score': [234,24,14,27,-74,46,73,-18,59,160]}
df = pd.DataFrame(data)
df
score
0 234
1 24
2 14
3 27
4 -74
5 46
6 73
7 -18
8 59
9 160
# 查看为未规范化的数据
df['score'].plot(kind='bar')

# <matplotlib.axes._subplots.AxesSubplot at 0x11b9c88d0> 

png

# 创建 x,其中 x 的得分列的值为浮点数
x = df[['score']].values.astype(float)

# 创建 minmax 处理器对象
min_max_scaler = preprocessing.MinMaxScaler()

# 创建一个对象,转换数据,拟合 minmax 处理器
x_scaled = min_max_scaler.fit_transform(x)

# 在数据帧上运行规范化器
df_normalized = pd.DataFrame(x_scaled)

# 查看数据帧
df_normalized
0
0 1.000000
1 0.318182
2 0.285714
3 0.327922
4 0.000000
5 0.389610
6 0.477273
7 0.181818
8 0.431818
9 0.759740
# 绘制数据帧
df_normalized.plot(kind='bar')

# <matplotlib.axes._subplots.AxesSubplot at 0x11ba31c50> 

png

Pandas 中的级联表

# 导入模块
import pandas as pd

raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 
        'TestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'TestScore'])
df
regiment company TestScore
0 Nighthawks 1st 4
1 Nighthawks 1st 24
2 Nighthawks 2nd 31
3 Nighthawks 2nd 2
4 Dragoons 1st 3
5 Dragoons 1st 4
6 Dragoons 2nd 24
7 Dragoons 2nd 31
8 Scouts 1st 2
9 Scouts 1st 3
10 Scouts 2nd 2
11 Scouts 2nd 3
# 按公司和团队创建分组均值的透视表
pd.pivot_table(df, index=['regiment','company'], aggfunc='mean')
TestScore
regiment company
Dragoons 1st 3.5
2nd 27.5
Nighthawks 1st 14.0
2nd 16.5
Scouts 1st 2.5
2nd 2.5
# 按公司和团队创建分组计数的透视表
df.pivot_table(index=['regiment','company'], aggfunc='count')
TestScore
regiment company
Dragoons 1st 2
2nd 2
Nighthawks 1st 2
2nd 2
Scouts 1st 2
2nd 2

在 Pandas 中快速修改字符串列

我经常需要或想要改变一串字符串中所有项目的大小写(例如BRAZILBrazil等)。 有很多方法可以实现这一目标,但我已经确定这是最容易和最快的方法。

# 导入 pandas
import pandas as pd

# 创建名称的列表
first_names = pd.Series(['Steve Murrey', 'Jane Fonda', 'Sara McGully', 'Mary Jane'])

# 打印列
first_names

'''
0    Steve Murrey
1      Jane Fonda
2    Sara McGully
3       Mary Jane
dtype: object 
'''

# 打印列的小写
first_names.str.lower()

'''
0    steve murrey
1      jane fonda
2    sara mcgully
3       mary jane
dtype: object 
'''

# 打印列的大写
first_names.str.upper()

'''
0    STEVE MURREY
1      JANE FONDA
2    SARA MCGULLY
3       MARY JANE
dtype: object 
'''

# 打印列的标题大小写
first_names.str.title()

'''
0    Steve Murrey
1      Jane Fonda
2    Sara Mcgully
3       Mary Jane
dtype: object 
'''

# 打印以空格分割的列
first_names.str.split(" ")

'''
0    [Steve, Murrey]
1      [Jane, Fonda]
2    [Sara, McGully]
3       [Mary, Jane]
dtype: object 
'''

# 打印首字母大写的列
first_names.str.capitalize()

'''
0    Steve murrey
1      Jane fonda
2    Sara mcgully
3       Mary jane
dtype: object 
'''

明白了吧。更多字符串方法在这里

随机抽样数据帧

# 导入模块
import pandas as pd
import numpy as np

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df
first_name last_name age preTestScore postTestScore
0 Jason Miller 42 4 25
1 Molly Jacobson 52 24 94
2 Tina Ali 36 31 57
3 Jake Milner 24 2 62
4 Amy Cooze 73 3 70
# 不放回选择大小为 2 的随机子集
df.take(np.random.permutation(len(df))[:2])
first_name last_name age preTestScore postTestScore
1 Molly Jacobson 52 24 94
4 Amy Cooze 73 3 70

对数据帧的行排名

# 导入模块
import pandas as pd

# 创建数据帧
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3],
        'coverage': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
coverage name reports year
Cochice 25 Jason 4 2012
Pima 94 Molly 24 2012
Santa Cruz 57 Tina 31 2013
Maricopa 62 Jake 2 2014
Yuma 70 Amy 3 2014

5 rows × 4 columns

# 创建一个新列,该列是 coverage 值的升序排名
df['coverageRanked'] = df['coverage'].rank(ascending=1)
df
coverage name reports year coverageRanked
Cochice 25 Jason 4 2012 1
Pima 94 Molly 24 2012 5
Santa Cruz 57 Tina 31 2013 2
Maricopa 62 Jake 2 2014 3
Yuma 70 Amy 3 2014 4

5 rows × 5 columns

正则表达式基础

# 导入正则包
import re

import sys

text = 'The quick brown fox jumped over the lazy black bear.'

three_letter_word = '\w{3}'

pattern_re = re.compile(three_letter_word); pattern_re

re.compile(r'\w{3}', re.UNICODE) 

re_search = re.search('..own', text)

if re_search:
    # 打印搜索结果
    print(re_search.group())

# brown 

re.match

re.match()仅用于匹配字符串的开头或整个字符串。对于其他任何内容,请使用re.search

Match all three letter words in text

# 在文本中匹配所有三个字母的单词
re_match = re.match('..own', text)

if re_match:
    # 打印所有匹配
    print(re_match.group())
else:
    # 打印这个
    print('No matches')

# No matches 

re.split

# 使用 'e' 作为分隔符拆分字符串。
re_split = re.split('e', text); re_split

# ['Th', ' quick brown fox jump', 'd ov', 'r th', ' lazy black b', 'ar.'] 

re.sub

用其他东西替换正则表达式模式串。3表示要进行的最大替换次数。

# 用 'E' 替换前三个 'e' 实例,然后打印出来
re_sub = re.sub('e', 'E', text, 3); print(re_sub)

# ThE quick brown fox jumpEd ovEr the lazy black bear. 

正则表达式示例

# 导入 regex
import re

# 创建一些数据
text = 'A flock of 120 quick brown foxes jumped over 30 lazy brown, bears.'

re.findall('^A', text)

# ['A'] 

re.findall('bears.$', text)

# ['bears.'] 

re.findall('f..es', text)

# ['foxes'] 

# 寻找所有元音
re.findall('[aeiou]', text)

# ['o', 'o', 'u', 'i', 'o', 'o', 'e', 'u', 'e', 'o', 'e', 'a', 'o', 'e', 'a'] 

# 查找不是小写元音的所有字符
re.findall('[^aeiou]', text)

'''
['A',
 ' ',
 'f',
 'l',
 'c',
 'k',
 ' ',
 'f',
 ' ',
 '1',
 '2',
 '0',
 ' ',
 'q',
 'c',
 'k',
 ' ',
 'b',
 'r',
 'w',
 'n',
 ' ',
 'f',
 'x',
 's',
 ' ',
 'j',
 'm',
 'p',
 'd',
 ' ',
 'v',
 'r',
 ' ',
 '3',
 '0',
 ' ',
 'l',
 'z',
 'y',
 ' ',
 'b',
 'r',
 'w',
 'n',
 ',',
 ' ',
 'b',
 'r',
 's',
 '.'] 
'''

re.findall('a|A', text)

# ['A', 'a', 'a'] 

# 寻找任何 'fox' 的实例
re.findall('(foxes)', text)

# ['foxes'] 

# 寻找所有五个字母的单词
re.findall('\w\w\w\w\w', text)

# ['flock', 'quick', 'brown', 'foxes', 'jumpe', 'brown', 'bears'] 

re.findall('\W\W', text)

# [', '] 

re.findall('\s', text)

# [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' '] 

re.findall('\S\S', text)

'''
['fl',
 'oc',
 'of',
 '12',
 'qu',
 'ic',
 'br',
 'ow',
 'fo',
 'xe',
 'ju',
 'mp',
 'ed',
 'ov',
 'er',
 '30',
 'la',
 'zy',
 'br',
 'ow',
 'n,',
 'be',
 'ar',
 's.'] 
'''

re.findall('\d\d\d', text)

# ['120'] 

re.findall('\D\D\D\D\D', text)

'''
['A flo',
 'ck of',
 ' quic',
 'k bro',
 'wn fo',
 'xes j',
 'umped',
 ' over',
 ' lazy',
 ' brow',
 'n, be'] 
'''

re.findall('\AA', text)

# ['A'] 

re.findall('bears.\Z', text)

# ['bears.'] 

re.findall('\b[foxes]', text)

# [] 

re.findall('\n', text)

# [] 

re.findall('[Ff]oxes', 'foxes Foxes Doxes')

# ['foxes', 'Foxes'] 

re.findall('[Ff]oxes', 'foxes Foxes Doxes')

# ['foxes', 'Foxes'] 

re.findall('[a-z]', 'foxes Foxes')

# ['f', 'o', 'x', 'e', 's', 'o', 'x', 'e', 's'] 

re.findall('[A-Z]', 'foxes Foxes')

# ['F'] 

re.findall('[a-zA-Z0-9]', 'foxes Foxes')

# ['f', 'o', 'x', 'e', 's', 'F', 'o', 'x', 'e', 's'] 

re.findall('[^aeiou]', 'foxes Foxes')

# ['f', 'x', 's', ' ', 'F', 'x', 's'] 

re.findall('[^0-9]', 'foxes Foxes')

# ['f', 'o', 'x', 'e', 's', ' ', 'F', 'o', 'x', 'e', 's'] 

re.findall('foxes?', 'foxes Foxes')

# ['foxes'] 

re.findall('ox*', 'foxes Foxes')

# ['ox', 'ox'] 

re.findall('ox+', 'foxes Foxes')

# ['ox', 'ox'] 

re.findall('\d{3}', text)

# ['120'] 

re.findall('\d{2,}', text)

# ['120', '30'] 

re.findall('\d{2,3}', text)

# ['120', '30'] 

re.findall('^A', text)

# ['A'] 

re.findall('bears.$', text)

# ['bears.'] 

re.findall('\AA', text)

# ['A'] 

re.findall('bears.\Z', text)

# ['bears.'] 

re.findall('bears(?=.)', text)

# ['bears'] 

re.findall('foxes(?!!)', 'foxes foxes!')

# ['foxes'] 

re.findall('foxes|foxes!', 'foxes foxes!')

# ['foxes', 'foxes'] 

re.findall('fox(es!)', 'foxes foxes!')

# ['es!'] 

re.findall('foxes(!)', 'foxes foxes!')

# ['!'] 

重索引序列和数据帧

# 导入模块
import pandas as pd
import numpy as np

# 创建亚利桑那州南部的火灾风险序列
brushFireRisk = pd.Series([34, 23, 12, 23], index = ['Bisbee', 'Douglas', 'Sierra Vista', 'Tombstone'])
brushFireRisk

'''
Bisbee          34
Douglas         23
Sierra Vista    12
Tombstone       23
dtype: int64 
'''

# 重索引这个序列并创建一个新的序列变量
brushFireRiskReindexed = brushFireRisk.reindex(['Tombstone', 'Douglas', 'Bisbee', 'Sierra Vista', 'Barley', 'Tucson'])
brushFireRiskReindexed

'''
Tombstone       23.0
Douglas         23.0
Bisbee          34.0
Sierra Vista    12.0
Barley           NaN
Tucson           NaN
dtype: float64 
'''

# 重索引序列并在任何缺失的索引处填入 0
brushFireRiskReindexed = brushFireRisk.reindex(['Tombstone', 'Douglas', 'Bisbee', 'Sierra Vista', 'Barley', 'Tucson'], fill_value = 0)
brushFireRiskReindexed

'''
Tombstone       23
Douglas         23
Bisbee          34
Sierra Vista    12
Barley           0
Tucson           0
dtype: int64 
'''

# 创建数据帧
data = {'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data)
df
county reports year
0 Cochice 4 2012
1 Pima 24 2012
2 Santa Cruz 31 2013
3 Maricopa 2 2014
4 Yuma 3 2014
# 更改行的顺序(索引)
df.reindex([4, 3, 2, 1, 0])
county reports year
4 Yuma 3 2014
3 Maricopa 2 2014
2 Santa Cruz 31 2013
1 Pima 24 2012
0 Cochice 4 2012
# 更改列的顺序(索引)
columnsTitles = ['year', 'reports', 'county']
df.reindex(columns=columnsTitles)
year reports county
0 2012 4 Cochice
1 2012 24 Pima
2 2013 31 Santa Cruz
3 2014 2 Maricopa
4 2014 3 Yuma

重命名列标题

来自 StackOverflow 上的 rgalbo

# 导入所需模块
import pandas as pd

# 创建列表的字典,作为值
raw_data = {'0': ['first_name', 'Molly', 'Tina', 'Jake', 'Amy'], 
        '1': ['last_name', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        '2': ['age', 52, 36, 24, 73], 
        '3': ['preTestScore', 24, 31, 2, 3]}

# 创建数据帧
df = pd.DataFrame(raw_data)

# 查看数据帧
df
0 1 2 3
0 first_name last_name age preTestScore
1 Molly Jacobson 52 24
2 Tina Ali 36 31
3 Jake Milner 24 2
4 Amy Cooze 73 3
# 从数据集的第一行创建一个名为 header 的新变量
header = df.iloc[0]

'''
0      first_name
1       last_name
2             age
3    preTestScore
Name: 0, dtype: object 
'''

# 将数据帧替换为不包含第一行的新数据帧
df = df[1:]

# 使用标题变量重命名数据帧的列值
df.rename(columns = header)
first_name last_name age preTestScore
1 Molly Jacobson 52 24
--- --- --- --- ---
2 Tina Ali 36 31
--- --- --- --- ---
3 Jake Milner 24 2
--- --- --- --- ---
4 Amy Cooze 73 3
--- --- --- --- ---

重命名多个数据帧的列名

# 导入模块
import pandas as pd

# 设置 ipython 的最大行显示
pd.set_option('display.max_row', 1000)

# 设置 ipython 的最大列宽
pd.set_option('display.max_columns', 50)

# 创建示例数据帧
data = {'Commander': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'Date': ['2012, 02, 08', '2012, 02, 08', '2012, 02, 08', '2012, 02, 08', '2012, 02, 08'], 
        'Score': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
Commander Date Score
Cochice Jason 2012, 02, 08 4
Pima Molly 2012, 02, 08 24
Santa Cruz Tina 2012, 02, 08 31
Maricopa Jake 2012, 02, 08 2
Yuma Amy 2012, 02, 08 3
# 重命名列名
df.columns = ['Leader', 'Time', 'Score']

df
Leader Time Score
Cochice Jason 2012, 02, 08 4
Pima Molly 2012, 02, 08 24
Santa Cruz Tina 2012, 02, 08 31
Maricopa Jake 2012, 02, 08 2
Yuma Amy 2012, 02, 08 3
df.rename(columns={'Leader': 'Commander'}, inplace=True)

df
Commander Time Score
Cochice Jason 2012, 02, 08 4
Pima Molly 2012, 02, 08 24
Santa Cruz Tina 2012, 02, 08 31
Maricopa Jake 2012, 02, 08 2
Yuma Amy 2012, 02, 08 3

替换值

# 导入模块
import pandas as pd
import numpy as np

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [-999, -999, -999, 2, 1],
        'postTestScore': [2, 2, -999, 2, -999]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df
first_name last_name age preTestScore postTestScore
0 Jason Miller 42 -999 2
1 Molly Jacobson 52 -999 2
2 Tina Ali 36 -999 -999
3 Jake Milner 24 2 2
4 Amy Cooze 73 1 -999
# 将所有 -999 替换为 NAN
df.replace(-999, np.nan)
first_name last_name age preTestScore postTestScore
0 Jason Miller 42 NaN 2.0
1 Molly Jacobson 52 NaN 2.0
2 Tina Ali 36 NaN NaN
3 Jake Milner 24 2.0 2.0
4 Amy Cooze 73 1.0 NaN

将数据帧保存为 CSV

# 导入模块
import pandas as pd

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df
first_name last_name age preTestScore postTestScore
0 Jason Miller 42 4 25
1 Molly Jacobson 52 24 94
2 Tina Ali 36 31 57
3 Jake Milner 24 2 62
4 Amy Cooze 73 3 70

将名为df的数据帧保存为 csv。

df.to_csv('example.csv')

在列中搜索某个值

# 导入模块
import pandas as pd

raw_data = {'first_name': ['Jason', 'Jason', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Miller', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 42, 36, 24, 73], 
        'preTestScore': [4, 4, 31, 2, 3],
        'postTestScore': [25, 25, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df
first_name last_name age preTestScore postTestScore
0 Jason Miller 42 4 25
1 Jason Miller 42 4 25
2 Tina Ali 36 31 57
3 Jake Milner 24 2 62
4 Amy Cooze 73 3 70
# 在列中寻找值在哪里
# 查看 postTestscore 大于 50 的地方
df['preTestScore'].where(df['postTestScore'] > 50)

'''
0     NaN
1     NaN
2    31.0
3     2.0
4     3.0
Name: preTestScore, dtype: float64 
'''

选择包含特定值的行和列

# 导入模块
import pandas as pd

# 设置 ipython 的最大行显示
pd.set_option('display.max_row', 1000)

# 设置 ipython 的最大列宽
pd.set_option('display.max_columns', 50)

# 创建示例数据帧
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014
# 按照列值抓取行
value_list = ['Tina', 'Molly', 'Jason']

df[df.name.isin(value_list)]
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
# 获取列值不是某个值的行
df[~df.name.isin(value_list)]
name reports year
Maricopa Jake 2 2014
Yuma Amy 3 2014

选择具有特定值的行

import pandas as pd

# 创建示例数据帧
data = {'name': ['Jason', 'Molly'], 
        'country': [['Syria', 'Lebanon'],['Spain', 'Morocco']]}
df = pd.DataFrame(data)
df
country name
0 [Syria, Lebanon] Jason
1 [Spain, Morocco] Molly
df[df['country'].map(lambda country: 'Syria' in country)]
country name
0 [Syria, Lebanon] Jason

使用多个过滤器选择行

import pandas as pd

# 创建示例数据帧
data = {'name': ['A', 'B', 'C', 'D', 'E'], 
        'score': [1,2,3,4,5]}
df = pd.DataFrame(data)
df
name score
0 A 1
1 B 2
2 C 3
3 D 4
4 E 5
# 选择数据帧的行,其中 df.score 大于 1 且小于 5
df[(df['score'] > 1) & (df['score'] < 5)]
name score
1 B 2
2 C 3
3 D 4

根据条件选择数据帧的行

# 导入模块
import pandas as pd
import numpy as np

# 创建数据帧
raw_data = {'first_name': ['Jason', 'Molly', np.nan, np.nan, np.nan], 
        'nationality': ['USA', 'USA', 'France', 'UK', 'UK'], 
        'age': [42, 52, 36, 24, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'nationality', 'age'])
df
first_name nationality age
0 Jason USA 42
1 Molly USA 52
2 NaN France 36
3 NaN UK 24
4 NaN UK 70
# 方法 1:使用布尔变量
# 如果国籍是美国,则变量为 TRUE
american = df['nationality'] == "USA"

# 如果年龄大于 50,则变量为 TRUE
elderly = df['age'] > 50

# 选择所有国籍为美国且年龄大于 50 的案例
df[american & elderly]
first_name nationality age
1 Molly USA 52
# 方法 2:使用变量属性
# 选择所有不缺少名字且国籍为美国的案例
df[df['first_name'].notnull() & (df['nationality'] == "USA")]
first_name nationality age
0 Jason USA 42
1 Molly USA 52

数据帧简单示例

# 导入模块
import pandas as pd

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df
first_name last_name age preTestScore postTestScore
0 Jason Miller 42 4 25
1 Molly Jacobson 52 24 94
2 Tina Ali 36 31 57
3 Jake Milner 24 2 62
4 Amy Cooze 73 3 70
# 创建第二个数据帧
raw_data_2 = {'first_name': ['Sarah', 'Gueniva', 'Know', 'Sara', 'Cat'], 
        'last_name': ['Mornig', 'Jaker', 'Alom', 'Ormon', 'Koozer'], 
        'age': [53, 26, 72, 73, 24], 
        'preTestScore': [13, 52, 72, 26, 26],
        'postTestScore': [82, 52, 56, 234, 254]}
df_2 = pd.DataFrame(raw_data_2, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df_2
first_name last_name age preTestScore postTestScore
0 Sarah Mornig 53 13 82
1 Gueniva Jaker 26 52 52
2 Know Alom 72 72 56
3 Sara Ormon 73 26 234
4 Cat Koozer 24 26 254
# 创建第三个数据帧
raw_data_3 = {'first_name': ['Sarah', 'Gueniva', 'Know', 'Sara', 'Cat'], 
        'last_name': ['Mornig', 'Jaker', 'Alom', 'Ormon', 'Koozer'],
         'postTestScore_2': [82, 52, 56, 234, 254]}
df_3 = pd.DataFrame(raw_data_3, columns = ['first_name', 'last_name', 'postTestScore_2'])
df_3
first_name last_name postTestScore_2
0 Sarah Mornig 82
1 Gueniva Jaker 52
2 Know Alom 56
3 Sara Ormon 234
4 Cat Koozer 254

排序数据帧的行

# 导入模块
import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [1, 2, 1, 2, 3],
        'coverage': [2, 2, 3, 3, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
coverage name reports year
Cochice 2 Jason 1 2012
Pima 2 Molly 2 2012
Santa Cruz 3 Tina 1 2013
Maricopa 3 Jake 2 2014
Yuma 3 Amy 3 2014
# 按报告对数据框的行降序排序
df.sort_values(by='reports', ascending=0)
coverage name reports year
Yuma 3 Amy 3 2014
Pima 2 Molly 2 2012
Maricopa 3 Jake 2 2014
Cochice 2 Jason 1 2012
Santa Cruz 3 Tina 1 2013
# 按 coverage 然后是报告对数据帧的行升序排序
df.sort_values(by=['coverage', 'reports'])
coverage name reports year
Cochice 2 Jason 1 2012
Pima 2 Molly 2 2012
Santa Cruz 3 Tina 1 2013
Maricopa 3 Jake 2 2014
Yuma 3 Amy 3 2014

将经纬度坐标变量拆分为单独的变量

import pandas as pd
import numpy as np

raw_data = {'geo': ['40.0024, -105.4102', '40.0068, -105.266', '39.9318, -105.2813', np.nan]}
df = pd.DataFrame(raw_data, columns = ['geo'])
df
geo
0 40.0024, -105.4102
1 40.0068, -105.266
2 39.9318, -105.2813
3 NaN
--- ---
# 为要放置的循环结果创建两个列表
lat = []
lon = []

# 对于变量中的每一行
for row in df['geo']:
    # Try to,
    try:
        # 用逗号分隔行,转换为浮点
        # 并将逗号前的所有内容追加到 lat
        lat.append(row.split(',')[0])
        # 用逗号分隔行,转换为浮点
        # 并将逗号后的所有内容追加到 lon
        lon.append(row.split(',')[1])
    # 但是如果你得到了错误
    except:
        # 向 lat 添加缺失值
        lat.append(np.NaN)
        # 向 lon 添加缺失值
        lon.append(np.NaN)

# 从 lat 和 lon 创建新的两列
df['latitude'] = lat
df['longitude'] = lon

df
geo latitude longitude
0 40.0024, -105.4102 40.0024 -105.4102
1 40.0068, -105.266 40.0068 -105.266
2 39.9318, -105.2813 39.9318 -105.2813
3 NaN NaN NaN

数据流水线

# 创建一些原始数据
raw_data = [1,2,3,4,5,6,7,8,9,10]

# 定义产生 input+6 的生成器
def add_6(numbers):
    for x in numbers:
        output = x+6
        yield output

# 定义产生 input-2 的生成器
def subtract_2(numbers):
    for x in numbers:
        output = x-2
        yield output

# 定义产生 input*100 的生成器
def multiply_by_100(numbers):
    for x in numbers:
        output = x*100
        yield output

# 流水线的第一步
step1 = add_6(raw_data)

# 流水线的第二步
step2 = subtract_2(step1)

# 流水线的第三步
pipeline = multiply_by_100(step2)

# 原始数据的第一个元素
next(pipeline)

# 500 

# 原始数据的第二个元素
next(pipeline)

# 600 

# 处理所有数据
for raw_data in pipeline:
    print(raw_data)

'''
700
800
900
1000
1100
1200
1300
1400
'''

数据帧中的字符串整理

# 导入模块
import pandas as pd
import numpy as np
import re as re

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'email': ['[[email protected]](/cdn-cgi/l/email-protection)', '[[email protected]](/cdn-cgi/l/email-protection)', np.NAN, '[[email protected]](/cdn-cgi/l/email-protection)', '[[email protected]](/cdn-cgi/l/email-protection)'], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'email', 'preTestScore', 'postTestScore'])
df
first_name last_name email preTestScore postTestScore
0 Jason Miller [email protected] 4 25
1 Molly Jacobson [email protected] 24 94
2 Tina Ali NaN 31 57
3 Jake Milner [email protected] 2 62
4 Amy Cooze [email protected] 3 70
# 电子邮件列中的哪些字符串包含 'gmail'
df['email'].str.contains('gmail')

'''
0     True
1     True
2      NaN
3    False
4    False
Name: email, dtype: object 
'''

pattern = '([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\.([A-Z]{2,4})'

df['email'].str.findall(pattern, flags=re.IGNORECASE)

'''
0       [(jas203, gmail, com)]
1    [(momomolly, gmail, com)]
2                          NaN
3     [(battler, milner, com)]
4     [(Ames1234, yahoo, com)]
Name: email, dtype: object 
'''

matches = df['email'].str.match(pattern, flags=re.IGNORECASE)
matches

'''
/Users/chrisralbon/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: In future versions of pandas, match will change to always return a bool indexer.
  if __name__ == '__main__':

0       (jas203, gmail, com)
1    (momomolly, gmail, com)
2                        NaN
3     (battler, milner, com)
4     (Ames1234, yahoo, com)
Name: email, dtype: object 
'''

matches.str[1]

'''
0     gmail
1     gmail
2       NaN
3    milner
4     yahoo
Name: email, dtype: object 
'''

和 Pandas 一起使用列表推导式

# 导入模块
import pandas as pd

# 设置 ipython 的最大行显示
pd.set_option('display.max_row', 1000)

# 设置 ipython 的最大列宽
pd.set_option('display.max_columns', 50)

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df
name reports year
Cochice Jason 4 2012
Pima Molly 24 2012
Santa Cruz Tina 31 2013
Maricopa Jake 2 2014
Yuma Amy 3 2014

作为循环的列表推导式。

# 创建变量
next_year = []

# 对于 df.years 的每一行
for row in df['year']:
    # 为这一行添加 1 并将其附加到 next_year
    next_year.append(row + 1)

# 创建 df.next_year
df['next_year'] = next_year

# 查看数据帧
df
name reports year next_year
Cochice Jason 4 2012 2013
Pima Molly 24 2012 2013
Santa Cruz Tina 31 2013 2014
Maricopa Jake 2 2014 2015
Yuma Amy 3 2014 2015

作为列表推导式。

# 对于 df.year 中的每一行,从行中减去 1
df['previous_year'] = [row-1 for row in df['year']]

df
name reports year next_year previous_year
Cochice Jason 4 2012 2013 2011
Pima Molly 24 2012 2013 2011
Santa Cruz Tina 31 2013 2014 2012
Maricopa Jake 2 2014 2015 2013
Yuma Amy 3 2014 2015 2013

使用 Seaborn 来可视化数据帧

import pandas as pd
%matplotlib inline
import random
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame()

df['x'] = random.sample(range(1, 100), 25)
df['y'] = random.sample(range(1, 100), 25)

df.head()
x y
0 18 25
1 42 67
2 52 77
3 4 34
4 14 69
# 散点图
sns.lmplot('x', 'y', data=df, fit_reg=False)

# <seaborn.axisgrid.FacetGrid at 0x114563b00> 

png

# 密度图
sns.kdeplot(df.y)

# <matplotlib.axes._subplots.AxesSubplot at 0x113ea2ef0> 

png

sns.kdeplot(df.y, df.x)

# <matplotlib.axes._subplots.AxesSubplot at 0x113d7fef0> 

png

sns.distplot(df.x)

# <matplotlib.axes._subplots.AxesSubplot at 0x114294160> 

png

# 直方图
plt.hist(df.x, alpha=.3)
sns.rugplot(df.x);

png

# 箱形图
sns.boxplot([df.y, df.x])

# <matplotlib.axes._subplots.AxesSubplot at 0x1142b8b38> 

png

# 提琴图
sns.violinplot([df.y, df.x])

# <matplotlib.axes._subplots.AxesSubplot at 0x114444a58> 

png

# 热力图
sns.heatmap([df.y, df.x], annot=True, fmt="d")

# <matplotlib.axes._subplots.AxesSubplot at 0x114530c88> 

png

# 聚类图
sns.clustermap(df)

# <seaborn.matrix.ClusterGrid at 0x116f313c8> 

png

Pandas 数据结构

# 导入模块
import pandas as pd

序列 101

序列是一维数组(类似 R 的向量)。

# 创建 floodingReports 数量的序列
floodingReports = pd.Series([5, 6, 2, 9, 12])
floodingReports

'''
0     5
1     6
2     2
3     9
4    12
dtype: int64 
'''

请注意,第一列数字(0 到 4)是索引。

# 将县名设置为 floodingReports 序列的索引
floodingReports = pd.Series([5, 6, 2, 9, 12], index=['Cochise County', 'Pima County', 'Santa Cruz County', 'Maricopa County', 'Yuma County'])
floodingReports

'''
Cochise County        5
Pima County           6
Santa Cruz County     2
Maricopa County       9
Yuma County          12
dtype: int64 
'''

floodingReports['Cochise County']

# 5 

floodingReports[floodingReports > 6]

'''
Maricopa County     9
Yuma County        12
dtype: int64 
'''

从字典中创建 Pandas 序列。

注意:执行此操作时,字典的键将成为序列索引。

# 创建字典
fireReports_dict = {'Cochise County': 12, 'Pima County': 342, 'Santa Cruz County': 13, 'Maricopa County': 42, 'Yuma County' : 52}

# 将字典转换为 pd.Series,然后查看它
fireReports = pd.Series(fireReports_dict); fireReports

'''
Cochise County        12
Maricopa County       42
Pima County          342
Santa Cruz County     13
Yuma County           52
dtype: int64 
'''

fireReports.index = ["Cochice", "Pima", "Santa Cruz", "Maricopa", "Yuma"]
fireReports

'''
Cochice        12
Pima           42
Santa Cruz    342
Maricopa       13
Yuma           52
dtype: int64 
'''

数据帧 101

数据帧就像 R 的数据帧。

# 从等长列表或 NumPy 数组的字典中创建数据帧
data = {'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data)
df
county reports year
0 Cochice 4 2012
1 Pima 24 2012
2 Santa Cruz 31 2013
3 Maricopa 2 2014
4 Yuma 3 2014
# 使用 columns 属性设置列的顺序
dfColumnOrdered = pd.DataFrame(data, columns=['county', 'year', 'reports'])
dfColumnOrdered
county year reports
0 Cochice 2012 4
1 Pima 2012 24
2 Santa Cruz 2013 31
3 Maricopa 2014 2
4 Yuma 2014 3
# 添加一列
dfColumnOrdered['newsCoverage'] = pd.Series([42.3, 92.1, 12.2, 39.3, 30.2])
dfColumnOrdered
county year reports newsCoverage
0 Cochice 2012 4 42.3
1 Pima 2012 24 92.1
2 Santa Cruz 2013 31 12.2
3 Maricopa 2014 2 39.3
4 Yuma 2014 3 30.2
# 删除一列
del dfColumnOrdered['newsCoverage']
dfColumnOrdered
county year reports
0 Cochice 2012 4
1 Pima 2012 24
2 Santa Cruz 2013 31
3 Maricopa 2014 2
4 Yuma 2014 3
# 转置数据帧
dfColumnOrdered.T
0 1 2 3 4
county Cochice Pima Santa Cruz Maricopa Yuma
year 2012 2012 2013 2014 2014
reports 4 24 31 2 3

Pandas 时间序列基础

# 导入模块
from datetime import datetime
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as pyplot

data = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994', '2014-05-02 18:47:05.178768', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.280592', '2014-05-03 18:47:05.332662', '2014-05-03 18:47:05.385109', '2014-05-04 18:47:05.436523', '2014-05-04 18:47:05.486877'], 
        'battle_deaths': [34, 25, 26, 15, 15, 14, 26, 25, 62, 41]}
df = pd.DataFrame(data, columns = ['date', 'battle_deaths'])
print(df)

'''
 date  battle_deaths
0  2014-05-01 18:47:05.069722             34
1  2014-05-01 18:47:05.119994             25
2  2014-05-02 18:47:05.178768             26
3  2014-05-02 18:47:05.230071             15
4  2014-05-02 18:47:05.230071             15
5  2014-05-02 18:47:05.280592             14
6  2014-05-03 18:47:05.332662             26
7  2014-05-03 18:47:05.385109             25
8  2014-05-04 18:47:05.436523             62
9  2014-05-04 18:47:05.486877             41 
'''

df['date'] = pd.to_datetime(df['date'])

df.index = df['date']
del df['date']
df
battle_deaths
date
2014-05-01 18:47:05.069722 34
2014-05-01 18:47:05.119994 25
2014-05-02 18:47:05.178768 26
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.280592 14
2014-05-03 18:47:05.332662 26
2014-05-03 18:47:05.385109 25
2014-05-04 18:47:05.436523 62
2014-05-04 18:47:05.486877 41
# 查看 2014 年的所有观测
df['2014']
battle_deaths
date
2014-05-01 18:47:05.069722 34
2014-05-01 18:47:05.119994 25
2014-05-02 18:47:05.178768 26
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.280592 14
2014-05-03 18:47:05.332662 26
2014-05-03 18:47:05.385109 25
2014-05-04 18:47:05.436523 62
2014-05-04 18:47:05.486877 41
# 查看 2014 年 5 月的所有观测
df['2014-05']
battle_deaths
date
2014-05-01 18:47:05.069722 34
2014-05-01 18:47:05.119994 25
2014-05-02 18:47:05.178768 26
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.280592 14
2014-05-03 18:47:05.332662 26
2014-05-03 18:47:05.385109 25
2014-05-04 18:47:05.436523 62
2014-05-04 18:47:05.486877 41
# 查看 2014.5.3 的所有观测
df[datetime(2014, 5, 3):]
battle_deaths
date
2014-05-03 18:47:05.332662 26
2014-05-03 18:47:05.385109 25
2014-05-04 18:47:05.436523 62
2014-05-04 18:47:05.486877 41

Observations between May 3rd and May 4th

# 查看 2014.5.3~4 的所有观测
df['5/3/2014':'5/4/2014']
battle_deaths
date
2014-05-03 18:47:05.332662 26
2014-05-03 18:47:05.385109 25
2014-05-04 18:47:05.436523 62
2014-05-04 18:47:05.486877 41
# 截断 2014.5.2 之后的观测
df.truncate(after='5/3/2014')
battle_deaths
date
2014-05-01 18:47:05.069722 34
2014-05-01 18:47:05.119994 25
2014-05-02 18:47:05.178768 26
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.280592 14
# 2014.5 的观测
df['5-2014']
battle_deaths
date
2014-05-01 18:47:05.069722 34
2014-05-01 18:47:05.119994 25
2014-05-02 18:47:05.178768 26
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.230071 15
2014-05-02 18:47:05.280592 14
2014-05-03 18:47:05.332662 26
2014-05-03 18:47:05.385109 25
2014-05-04 18:47:05.436523 62
2014-05-04 18:47:05.486877 41
# 计算每个时间戳的观测数
df.groupby(level=0).count()
battle_deaths
date
2014-05-01 18:47:05.069722 1
2014-05-01 18:47:05.119994 1
2014-05-02 18:47:05.178768 1
2014-05-02 18:47:05.230071 2
2014-05-02 18:47:05.280592 1
2014-05-03 18:47:05.332662 1
2014-05-03 18:47:05.385109 1
2014-05-04 18:47:05.436523 1
2014-05-04 18:47:05.486877 1
# 每天的 battle_deaths 均值
df.resample('D').mean()
battle_deaths
date
2014-05-01 29.5
2014-05-02 17.5
2014-05-03 25.5
2014-05-04 51.5
# 每天的 battle_deaths 总数
df.resample('D').sum()
battle_deaths
date
2014-05-01 59
2014-05-02 70
2014-05-03 51
2014-05-04 103
# 绘制每天的总死亡人数
df.resample('D').sum().plot()

# <matplotlib.axes._subplots.AxesSubplot at 0x11187a940> 

png

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