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The Pandas library provides easy-to-use data structures and analysis tools for Python. It uses NumPy as a foundation and allows import as pd. Pandas has two main data structures: Series for 1D labeled arrays and DataFrame for 2D labeled data with columns that can be different types. DataFrames allow selection of data by label, position, and filtering with Boolean conditions. Data can be manipulated with arithmetic operations and alignment between Series/DataFrames.
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![PythonForDataScience Cheat Sheet
Pandas Basics
Learn Python for Data Science Interactively at www.DataCamp.com
Pandas
DataCamp
Learn Python for Data Science Interactively
Series
DataFrame
4
7
-5
3
d
c
b
a
A one-dimensional labeled array
capable of holding any data type
Index
Index
Columns
A two-dimensional labeled
data structure with columns
of potentially different types
The Pandas library is built on NumPy and provides easy-to-use
data structures and data analysis tools for the Python
programming language.
>>> import pandas as pd
Use the following import convention:
Pandas Data Structures
>>> s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd'])
>>> data = {'Country': ['Belgium', 'India', 'Brazil'],
'Capital': ['Brussels', 'New Delhi', 'Brasília'],
'Population': [11190846, 1303171035, 207847528]}
>>> df = pd.DataFrame(data,
columns=['Country', 'Capital', 'Population'])
Selection
>>> s['b'] Get one element
-5
>>> df[1:] Get subset of a DataFrame
Country Capital Population
1 India New Delhi 1303171035
2 Brazil Brasília 207847528
By Position
>>> df.iloc[[0],[0]] Select single value by row &
'Belgium' column
>>> df.iat([0],[0])
'Belgium'
By Label
>>> df.loc[[0], ['Country']] Select single value by row &
'Belgium' column labels
>>> df.at([0], ['Country'])
'Belgium'
By Label/Position
>>> df.ix[2] Select single row of
Country Brazil subset of rows
Capital Brasília
Population 207847528
>>> df.ix[:,'Capital'] Select a single column of
0 Brussels subset of columns
1 New Delhi
2 Brasília
>>> df.ix[1,'Capital'] Select rows and columns
'New Delhi'
Boolean Indexing
>>> s[~(s > 1)] Series s where value is not >1
>>> s[(s < -1) | (s > 2)] s where value is <-1 or >2
>>> df[df['Population']>1200000000] Use filter to adjust DataFrame
Setting
>>> s['a'] = 6 Set index a of Series s to 6
Applying Functions
>>> f = lambda x: x*2
>>> df.apply(f) Apply function
>>> df.applymap(f) Apply function element-wise
Retrieving Series/DataFrame Information
>>> df.shape (rows,columns)
>>> df.index Describe index
>>> df.columns Describe DataFrame columns
>>> df.info() Info on DataFrame
>>> df.count() Number of non-NA values
Getting
Also see NumPy Arrays
Selecting, Boolean Indexing & Setting Basic Information
Summary
>>> df.sum() Sum of values
>>> df.cumsum() Cummulative sum of values
>>> df.min()/df.max() Minimum/maximum values
>>> df.idxmin()/df.idxmax() Minimum/Maximum index value
>>> df.describe() Summary statistics
>>> df.mean() Mean of values
>>> df.median() Median of values
Dropping
>>> s.drop(['a', 'c']) Drop values from rows (axis=0)
>>> df.drop('Country', axis=1) Drop values from columns(axis=1)
Data Alignment
>>> s.add(s3, fill_value=0)
a 10.0
b -5.0
c 5.0
d 7.0
>>> s.sub(s3, fill_value=2)
>>> s.div(s3, fill_value=4)
>>> s.mul(s3, fill_value=3)
>>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd'])
>>> s + s3
a 10.0
b NaN
c 5.0
d 7.0
Arithmetic Operations with Fill Methods
Internal Data Alignment
NA values are introduced in the indices that don’t overlap:
You can also do the internal data alignment yourself with
the help of the fill methods:
Sort & Rank
>>> df.sort_index() Sort by labels along an axis
>>> df.sort_values(by='Country') Sort by the values along an axis
>>> df.rank() Assign ranks to entries
Belgium Brussels
India New Delhi
Brazil Brasília
0
1
2
Country Capital
11190846
1303171035
207847528
Population
I/O
Read and Write to CSV
>>> pd.read_csv('file.csv', header=None, nrows=5)
>>> df.to_csv('myDataFrame.csv')
Read and Write to Excel
>>> pd.read_excel('file.xlsx')
>>> pd.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1')
Read multiple sheets from the same file
>>> xlsx = pd.ExcelFile('file.xls')
>>> df = pd.read_excel(xlsx, 'Sheet1')
>>> help(pd.Series.loc)
Asking For Help
Read and Write to SQL Query or Database Table
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite:///:memory:')
>>> pd.read_sql("SELECT * FROM my_table;", engine)
>>> pd.read_sql_table('my_table', engine)
>>> pd.read_sql_query("SELECT * FROM my_table;", engine)
>>> pd.to_sql('myDf', engine)
read_sql()is a convenience wrapper around read_sql_table() and
read_sql_query()](https://image.slidesharecdn.com/2pandasbasic-210204025521/85/2-pandasbasic-1-320.jpg)
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Pandas is a Python library used for data manipulation and analysis. It has powerful data structures like Series, DataFrames, and Panels. A Series is a one-dimensional array, DataFrame is a two-dimensional array that allows columns of different types, and Panel is a three-dimensional array. DataFrames can be created from lists, dictionaries, and other DataFrames. Columns can be added, deleted, sliced and concatenated. Categorical data types can be used to handle repetitive string values.
Spark Dataframe - Mr. Jyotiska
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• Can be constructed from existing RDDs or external data sources.
• Can scale from small datasets to TBs/PBs on multi-node Spark clusters.
• APIs available in Python, Java, Scala and R.
• Bytecode generation and optimization using Catalyst Optimizer.
• Simpler DSL to perform complex and data heavy operations.
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The document discusses various techniques for string manipulation in Python. It covers common string operations like concatenation, slicing, searching, replacing, formatting, splitting, stripping whitespace, and case conversion. Specific methods and functions are provided for each technique using Python's built-in string methods. Examples are given to demonstrate how to use string manipulation methods like find(), replace(), split(), strip(), lower(), upper(), etc. to perform various string operations in Python.
R Programming.pptx
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XII - 2022-23 - IP - RAIPUR (CBSE FINAL EXAM).pdf
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Python Pandas
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
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https://serudsindia.org/how-to-donate-to-charity-during-this-holiday-season/
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原版制作(DPU毕业证书)德保罗大学毕业证Offer一模一样
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
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◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
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◇企事业单位必须要求办理的
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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To date, the following regional organisations maintain or share lists of vessels that have been found to carry out or support IUU fishing within their own or adjacent convention areas and/or species of competence:
Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR)
Commission for the Conservation of Southern Bluefin Tuna (CCSBT)
General Fisheries Commission for the Mediterranean (GFCM)
Inter-American Tropical Tuna Commission (IATTC)
International Commission for the Conservation of Atlantic Tunas (ICCAT)
Indian Ocean Tuna Commission (IOTC)
Northwest Atlantic Fisheries Organisation (NAFO)
North East Atlantic Fisheries Commission (NEAFC)
North Pacific Fisheries Commission (NPFC)
South East Atlantic Fisheries Organisation (SEAFO)
South Pacific Regional Fisheries Management Organisation (SPRFMO)
Southern Indian Ocean Fisheries Agreement (SIOFA)
Western and Central Pacific Fisheries Commission (WCPFC)
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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加急办理华威大学毕业证硕士文凭证书原版一模一样
原版一模一样【微信：741003700 】【华威大学毕业证硕士文凭证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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State crafting: Changes and challenges for managing the public finances
2 pandasbasic
- 1. PythonForDataScience Cheat Sheet Pandas Basics Learn Python for Data Science Interactively at www.DataCamp.com Pandas DataCamp Learn Python for Data Science Interactively Series DataFrame 4 7 -5 3 d c b a A one-dimensional labeled array capable of holding any data type Index Index Columns A two-dimensional labeled data structure with columns of potentially different types The Pandas library is built on NumPy and provides easy-to-use data structures and data analysis tools for the Python programming language. >>> import pandas as pd Use the following import convention: Pandas Data Structures >>> s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd']) >>> data = {'Country': ['Belgium', 'India', 'Brazil'], 'Capital': ['Brussels', 'New Delhi', 'Brasília'], 'Population': [11190846, 1303171035, 207847528]} >>> df = pd.DataFrame(data, columns=['Country', 'Capital', 'Population']) Selection >>> s['b'] Get one element -5 >>> df[1:] Get subset of a DataFrame Country Capital Population 1 India New Delhi 1303171035 2 Brazil Brasília 207847528 By Position >>> df.iloc[[0],[0]] Select single value by row & 'Belgium' column >>> df.iat([0],[0]) 'Belgium' By Label >>> df.loc[[0], ['Country']] Select single value by row & 'Belgium' column labels >>> df.at([0], ['Country']) 'Belgium' By Label/Position >>> df.ix[2] Select single row of Country Brazil subset of rows Capital Brasília Population 207847528 >>> df.ix[:,'Capital'] Select a single column of 0 Brussels subset of columns 1 New Delhi 2 Brasília >>> df.ix[1,'Capital'] Select rows and columns 'New Delhi' Boolean Indexing >>> s[~(s > 1)] Series s where value is not >1 >>> s[(s < -1) | (s > 2)] s where value is <-1 or >2 >>> df[df['Population']>1200000000] Use filter to adjust DataFrame Setting >>> s['a'] = 6 Set index a of Series s to 6 Applying Functions >>> f = lambda x: x*2 >>> df.apply(f) Apply function >>> df.applymap(f) Apply function element-wise Retrieving Series/DataFrame Information >>> df.shape (rows,columns) >>> df.index Describe index >>> df.columns Describe DataFrame columns >>> df.info() Info on DataFrame >>> df.count() Number of non-NA values Getting Also see NumPy Arrays Selecting, Boolean Indexing & Setting Basic Information Summary >>> df.sum() Sum of values >>> df.cumsum() Cummulative sum of values >>> df.min()/df.max() Minimum/maximum values >>> df.idxmin()/df.idxmax() Minimum/Maximum index value >>> df.describe() Summary statistics >>> df.mean() Mean of values >>> df.median() Median of values Dropping >>> s.drop(['a', 'c']) Drop values from rows (axis=0) >>> df.drop('Country', axis=1) Drop values from columns(axis=1) Data Alignment >>> s.add(s3, fill_value=0) a 10.0 b -5.0 c 5.0 d 7.0 >>> s.sub(s3, fill_value=2) >>> s.div(s3, fill_value=4) >>> s.mul(s3, fill_value=3) >>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd']) >>> s + s3 a 10.0 b NaN c 5.0 d 7.0 Arithmetic Operations with Fill Methods Internal Data Alignment NA values are introduced in the indices that don’t overlap: You can also do the internal data alignment yourself with the help of the fill methods: Sort & Rank >>> df.sort_index() Sort by labels along an axis >>> df.sort_values(by='Country') Sort by the values along an axis >>> df.rank() Assign ranks to entries Belgium Brussels India New Delhi Brazil Brasília 0 1 2 Country Capital 11190846 1303171035 207847528 Population I/O Read and Write to CSV >>> pd.read_csv('file.csv', header=None, nrows=5) >>> df.to_csv('myDataFrame.csv') Read and Write to Excel >>> pd.read_excel('file.xlsx') >>> pd.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1') Read multiple sheets from the same file >>> xlsx = pd.ExcelFile('file.xls') >>> df = pd.read_excel(xlsx, 'Sheet1') >>> help(pd.Series.loc) Asking For Help Read and Write to SQL Query or Database Table >>> from sqlalchemy import create_engine >>> engine = create_engine('sqlite:///:memory:') >>> pd.read_sql("SELECT * FROM my_table;", engine) >>> pd.read_sql_table('my_table', engine) >>> pd.read_sql_query("SELECT * FROM my_table;", engine) >>> pd.to_sql('myDf', engine) read_sql()is a convenience wrapper around read_sql_table() and read_sql_query()