没有合适的资源？快使用搜索试试~ 我知道了~

文库首页大数据算法与数据结构Pandas Cheat Sheet

Pandas Cheat Sheet

Pandas

需积分: 13 15 下载量 76 浏览量 2018-01-25 10:54:06 上传评论收藏 539KB PDF 举报

温馨提示

试读

42页

# All categories (/pandas) | # Import, version (/pandas/import-version) | # Create data objects (/pandas/create-data-objects) | # View data info (/pandas/view-data-info) | # Visualize data (/pandas/visualize-data) | # Select data (/pandas/select-data) | # Manage unique & empty data (/pandas/manage-unique- empty-data) | # Modify & transform data (/pandas/modify-transform-data) | # Iterate over data (/pandas/iterate-over-data) | # Aggregate data (/pandas/aggregate-data) | # Save & Load (/pandas/save-load) |

资源推荐

资源详情

资源评论

Python 3 基础知识要点，方便复习、查询。适合打印2张A4纸，放在手边随学随查。 python 3 cheat sheet include lots of command quick look.

在这个"Python Data Science常用库 Cheat Sheet"中，我们涵盖了七个关键库：NumPy、SciPy、Pandas、Matplotlib，以及基础的Python知识，还有导入数据的相关技巧。下面我们将详细探讨这些库的功能和使用方法。 1. **...

2018/1/16 Pandas Cheat Sheet
https://mycheatsheets.com/pandas 1/42

 (mailto:?subject=Pandas%20Cheat%20Sheet&body=https%3A%2F%2Fmycheatsheets.com%2Fpandas)

 (https://twitter.com/share?url=https%3A%2F%2Fmycheatsheets.com%2Fpandas&text=Pandas%20Cheat%20Sheet)

 (https://facebook.com/sharer/sharer.php?u=https%3A%2F%2Fmycheatsheets.com%2Fpandas)

 (https://plus.google.com/share?url=https%3A%2F%2Fmycheatsheets.com%2Fpandas)

 (https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fmycheatsheets.com%2Fpandas)
11
38
See also:   NumPy Cheat Sheet (/numpy)  | Matplotlib Cheat Sheet (/matplotlib)  | All Cheat Sheets (/)
Pandas | MyCheatSheets.com (/pandas)
 
 |  #  All categories (/pandas) |  #  Import, version (/pandas/import-version) | 
#  Create data objects (/pandas/create-data-objects) |  #  View data info
(/pandas/view-data-info) |  #  Visualize data (/pandas/visualize-data) | #  Select data
(/pandas/select-data) |  #  Manage unique & empty data (/pandas/manage-unique-
empty-data) |  #  Modify & transform data (/pandas/modify-transform-data) | 
#  Iterate over data (/pandas/iterate-over-data) |  #  Aggregate data
(/pandas/aggregate-data) |  #  Save & Load (/pandas/save-load) | 
Pandas > All categories
Name
Code
⇨
Output
Add new columns (/pandas/dataframe-add-new-columns)
dataframe = pandas.DataFrame(numpy.random.randint(0,100,size=(5, 3)), 
columns=['C1','C2','C3'])
print ("dataframe")
print (dataframe)
print ("dataframe added new columns")
dataframe['C1p10']=dataframe['C1'] + 10
dataframe['C101']=101
print (dataframe)
⇨

 SEARCH


2018/1/16 Pandas Cheat Sheet

https://mycheatsheets.com/pandas 2/42

dataframe

C1 C2 C3

0 14 81 99

1 33 89 95

2 60 27 21

3 19 85 44

4 20 62 57

dataframe added new columns

C1 C2 C3 C1p10 C101

0 14 81 99 24 101

1 33 89 95 43 101

2 60 27 21 70 101

3 19 85 44 29 101

4 20 62 57 30 101

Append dataframes (/pandas/append-dataframes)

dataframe1 = pandas.DataFrame(numpy.random.randint(0,10,size=(3, 2)),

columns=['C1','C2'])

dataframe2 = pandas.DataFrame(numpy.random.randint(0,10,size=(3, 2)),

columns=['C1','C2'])

print ("dataframe1")

print (dataframe1)

print ("dataframe2")

print (dataframe2)

dataframe3 = dataframe1.append(dataframe2,ignore_index = True)

print ("appended dataframes")

print (dataframe3)

⇨

dataframe1

C1 C2

0 4 5

1 9 1

2 3 6

dataframe2

C1 C2

0 3 8

1 8 8

2 5 6

appended dataframes

C1 C2

0 4 5

1 9 1

2 3 6

3 3 8

4 8 8

5 5 6

Apply aggregate functions (/pandas/apply-aggregate-functions)

This website is using cookies.

We use them to give you the best experience. If

you continue using our website, we'll assume

that you are happy to receive all cookies on this

website.

2018/1/16 Pandas Cheat Sheet

https://mycheatsheets.com/pandas 3/42

dataframe = pandas.DataFrame(numpy.random.randint(0,10,size=(5, 3)),

columns=['C1','C2','C3'])

print ("dataframe")

print (dataframe)

print ("aggregates")

aggregates = dataframe.agg(['sum', 'max','mean'])

print (aggregates)

⇨

dataframe

C1 C2 C3

0 4 3 0

1 5 0 9

2 8 9 2

3 6 7 5

4 5 9 2

aggregates

C1 C2 C3

sum 28.0 28.0 18.0

max 8.0 9.0 9.0

mean 5.6 5.6 3.6

Apply function along axis (/pandas/apply-function-along-axis)

dataframe = pandas.DataFrame(numpy.random.randint(0,100,size=(5, 2)),

columns=['C1','C2'])

print ("dataframe")

print (dataframe)

# sum for columns

sum_columns = dataframe[['C1','C2']].apply(sum,axis=0)

print ("sum for columns")

print (sum_columns)

# sum for rows

sum_rows = dataframe[['C1','C2']].apply(sum,axis=1)

print ("sum for rows")

print (sum_rows)

⇨

Continue Learn more

(/init/default/terms)

This website is using cookies.

We use them to give you the best experience. If

you continue using our website, we'll assume

that you are happy to receive all cookies on this

website.

2018/1/16 Pandas Cheat Sheet

https://mycheatsheets.com/pandas 4/42

dataframe

C1 C2

0 56 2

1 94 89

2 0 56

3 62 80

4 80 98

sum for columns

C1 292

C2 325

dtype: int64

sum for rows

0 58

1 183

2 56

3 142

4 178

dtype: int64

Apply function to dataframe (map/lambda) (/pandas/dataframe-apply-function-

maplambda)

dataframe = pandas.DataFrame(numpy.random.randint(0,100,size=(5, 2)),

columns=['C1','C2'])

print ("dataframe")

print (dataframe)

# cross-tabulation of two factors (default is a frequency table)

dataframe['C1'] = dataframe['C1'].map(lambda x: x-100)

print ("modified dataframe")

print (dataframe)

⇨

dataframe

C1 C2

0 27 35

1 46 38

2 21 98

3 34 30

4 76 8

modified dataframe

C1 C2

0 -73 35

1 -54 38

2 -79 98

3 -66 30

4 -24 8

Apply function to each element (/pandas/dataframe-apply-function-to-each-

element)

Continue Learn more

(/init/default/terms)

This website is using cookies.

We use them to give you the best experience. If

you continue using our website, we'll assume

that you are happy to receive all cookies on this

website.

2018/1/16 Pandas Cheat Sheet

https://mycheatsheets.com/pandas 5/42

dataframe = pandas.DataFrame(numpy.random.randint(0,100,size=(5, 2)),

columns=['C1','C2'])

print ("dataframe")

print (dataframe)

function_result = dataframe.applymap(lambda x: x*10)

print ("apply result")

print (function_result)

⇨

dataframe

C1 C2

0 54 11

1 31 59

2 81 76

3 42 46

4 8 24

apply result

C1 C2

0 540 110

1 310 590

2 810 760

3 420 460

4 80 240

Calculate correlation (/pandas/calculate-dataframe-correlation)

dataframe = pandas.DataFrame(numpy.random.randint(0,100,size=(5, 3)),

columns=['C1','C2','C3'])

print ("dataframe")

print (dataframe)

print ("dataframe correlation")

print (dataframe.corr())

⇨

dataframe

C1 C2 C3

0 82 71 74

1 62 12 8

2 97 86 26

3 26 98 48

4 5 97 29

dataframe correlation

C1 C2 C3

C1 1.000000 -0.339904 0.107885

C2 -0.339904 1.000000 0.456666

C3 0.107885 0.456666 1.000000

Calculate covariance (/pandas/calculate-dataframe-covariance)

Continue Learn more

(/init/default/terms)

This website is using cookies.

We use them to give you the best experience. If

you continue using our website, we'll assume

that you are happy to receive all cookies on this

website.

剩余41页未读，继续阅读

评论收藏

内容反馈

资源评论

资源反馈

评论星级较低，若资源使用遇到问题可联系上传者，3个工作日内问题未解决可申请退款~

tjybrentao

粉丝: 0
资源: 2

上传资源快速赚钱

我的内容管理展开

我的资源快来上传第一个资源

我的收益

登录查看自己的收益

我的积分登录查看自己的积分

我的C币登录后查看C币余额

我的收藏

我的下载

下载帮助

前往需求广场，查看用户热搜

Pandas Cheat Sheet

pandas cheatsheet

pandas_cheat_sheet

Pandas_Cheat_Sheet.pdf

Python Pandas Cheat Sheet (DQ).pdf

Enthought Python Pandas Cheat Sheet.pdf

Jupyter Notebook Cheatsheet.pdf

probability and statistics cheatsheet 概率论与统计 总结

pandas全部操作指令表

python_cheatsheet_完美总结.pdf

Python 3 Cheat Sheet （Python小抄）

Cheat-Sheet

5.Pandas_Cheat_Sheet.pdf

Numpy and Pandas Cheat Sheet.pdf

Numpy and Pandas Cheat Sheet.zip

Enthought Python Pandas Cheat Sheet.zip

Data Wrangling with Pandas Cheat Sheet

Pandas_Cheat_Sheet--Python_For_Data_Science

Pandas Cheat Sheet (2021)-计算机科学

pandas、matplotlib、mysql入门常用代码精华合集cheatsheet

NumPy-SciPy-Pandas-Quandl-Cheat-Sheet

SQL-Cheat-Sheet

Scrum Cheat Sheet

Objective-C Cheat Sheet

Android-Cheat-Sheet

GDB-Cheat-Sheet

Python Data Science常用库 Cheat Sheet

RAPIDS cheatsheet.pdf

cheat sheet.zip

机器学习速查表（Cheat Sheet）.7z

最新资源

probability and statistics cheatsheet 概率论与统计总结