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1. Top results related to why are pandas called pandas?

   • Pandas: Why are my headers being inserted into the first row of my dataframe?

     Top Answer

     Answered Jan 03, 2019 · 1 votes

     Why? Because you've inserted the first row as data. The magic behaviour of using the first row as header is in read_csv(), if you create your dataframe without using read_csv, the first row is not treated specially.

     Solution? Skip the first row when inserting to the data frame generate by df_empty.

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     1/5

   • Pandas: Why are double brackets needed to select column after boolean indexing

     Top Answer

     Answered Oct 29, 2015 · 102 votes

     For pandas objects (Series, DataFrame), the indexing operator [] only accepts

     • colname or list of colnames to select column(s)
     • slicing or Boolean array to select row(s), i.e. it only refers to one dimension of the dataframe.

     For df[[colname(s)]], the interior brackets are for list, and the outside brackets are indexing operator, i.e. you must use double brackets if you select two or more columns. With one column name, single pair of brackets returns a Series, while double brackets return a dataframe.

     Also, df.ix[df['A'] < 3,['A','C']] or df.loc[df['A'] < 3,['A','C']] is better than the chained selection for avoiding returning a copy versus a view of the dataframe.

     Please refer pandas documentation for details

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     2/5

   • pandas - why the outputs of iloc and loc are different

     Top Answer

     Answered Jun 13, 2021 · 0 votes

     loc filters the dataframe based on the index column. For example, when you are calling df.loc[[100]], it will return the contents of the row with index value 100. iloc filters the dataframe based on the location. df.iloc[[100]] will return 100th row of your dataframe (numbering starts from 0). If the index of 100th row is 100, then df.iloc[[100]] and df.loc[[100]] will return the same row.

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     3/5

   • Pandas, are there any faster ways to update values?

     Top Answer

     Answered Nov 16, 2017 · 7 votes

     You can create dictionary first and then replace

     :

     #sample functiondef return_new_val(x): return x * 3-given_ids = list('abc')d = {_id: return_new_val(_id) for _id in given_ids}print (d){'a': 'aaa', 'c': 'ccc', 'b': 'bbb'}df = pd.DataFrame({'ID':list('abdefc'), 'M':[4,5,4,5,5,4]})df['3rd_col'] = df['ID'].replace(d)print (df) ID M 3rd_col0 a 4 aaa1 b 5 bbb2 d 4 d3 e 5 e4 f 5 f5 c 4 ccc

     Or map

     , but then get NaNs for no match:

     df['3rd_col'] = df['ID'].map(d)print (df)- ID M 3rd_col0 a 4 aaa1 b 5 bbb2 d 4 NaN3 e 5 NaN4 f 5 NaN5 c 4 ccc

     EDIT:

     If need append data by multiple functions first create new DataFrame and then join

     to original:

     def return_new_val1(x): return x * 2-def return_new_val2(x): return x * 3given_ids = list('abc')df2 = pd.DataFrame({'ID':given_ids})df2['3rd_col'] = df2['ID'].map(return_new_val1)df2['4rd_col'] = df2['ID'].map(return_new_val2)df2 = df2.set_index('ID')print (df2) 3rd_col 4rd_colID a aa aaab bb bbbc cc ccc

     df = pd.DataFrame({'ID':list('abdefc'), 'M':[4,5,4,5,5,4]})-df = df.join(df2, on='ID')print (df) ID M 3rd_col 4rd_col0 a 4 aa aaa1 b 5 bb bbb2 d 4 NaN NaN3 e 5 NaN NaN4 f 5 NaN NaN5 c 4 cc ccc#bur replace NaNs by values in `ID`cols = ['3rd_col','4rd_col']df[cols] = df[cols].mask(df[cols].isnull(), df['ID'], axis=0)print (df) ID M 3rd_col 4rd_col0 a 4 aa aaa1 b 5 bb bbb2 d 4 d d3 e 5 e e4 f 5 f f5 c 4 cc ccc

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     4/5

   • pandas - Why am I unable to skip rows using the "skiprows" parameter of Pandas

     Top Answer

     Answered Jan 06, 2018 · 1 votes

     I believe you need skip all rows by positions defined in list, row 10 is not in list, because data for Andorra. Data (row 1-8) before position defined in header (9) are excluded by default .

     Also parse_cols was replaced by usecols, because warning:

     FutureWarning: the 'parse_cols' keyword is deprecated, use 'usecols' instead parse_cols = "C:F"

     df=pd.read_excel('Energy Indicators.xls', sheet_name='Energy', skiprows=[10,12,13,14,15,16,17], skipfooter=38, header=9, usecols=[2,3,4,5] #parse_cols = "C:F" )print (df.head()) Country Energy Supply Energy Supply per capita \0 Andorra 9 121 1 Afghanistan 321 10 2 Albania 102 35 3 Algeria 1959 51 4 American Samoa ... ... Renewable Electricity Production 0 88.695650 1 78.669280 2 100.000000 3 0.551010 4 0.641026

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     5/5

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2. en.wikipedia.org › wiki › Pandas_(software)pandas (software) - Wikipedia

   en.wikipedia.org › wiki › Pandas_(software)

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   Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. [2]

3. People also ask

   What is pandas in Python?

   • Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. [ 2]

   pandas (software) - Wikipedia

   en.wikipedia.org/wiki/Pandas_(software)

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   Why is pandas important for data science and Data Engineering?

   • Developed by Wes McKinney in 2008, Pandas offers powerful, flexible, and easy-to-use data structures that have revolutionized how data scientists and analysts handle data. This article delves into why Pandas has become an indispensable tool in Python for data science, data analysis, and data engineering.

   Why Pandas is Used in Python - GeeksforGeeks

   www.geeksforgeeks.org/why-pandas-is-used-in-python/

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   What does pandas mean?

   • Pandas stands for ‘panel data’. Note that pandas is typically stylized as an all-lowercase word, although it is considered a best practice to capitalize its first letter at the beginning of sentences. Pandas is an open source library, which means that anyone can view its source code and make suggestions using pull requests.

   The Ultimate Guide to the Pandas Library for Data Science in Python

   www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/

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   What is pandas data structure in Python?

   • pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python. pandas has been used extensively in production in financial applications. Why more than one data structure? The best way to think about the pandas data structures is as flexible containers for lower dimensional data.

   Package overview — pandas 2.2.2 documentation

   pandas.pydata.org/docs/getting_started/overview.html

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4. www.nvidia.com › en-us › glossaryPandas Python– What Is It and Why Does It Matter? - NVIDIA

   www.nvidia.com › en-us › glossary

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   Pandas is the most popular software library for data manipulation and data analysis for the Python programming language. It strengthens Python’s ability to work with spreadsheet-like data with functionality that allows for fast loading, aligning, manipulating, and merging data.

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6. www.w3schools.com › python › pandasPandas Introduction - W3Schools

   www.w3schools.com › python › pandas

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   Why Use Pandas? Pandas allows us to analyze big data and make conclusions based on statistical theories. Pandas can clean messy data sets, and make them readable and relevant.

7. www.geeksforgeeks.org › why-pandas-is-used-in-pythonWhy Pandas is Used in Python - GeeksforGeeks

   www.geeksforgeeks.org › why-pandas-is-used-in-python

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   Jul 31, 2024 · Pandas has become an essential tool in the Python ecosystem due to its powerful data manipulation capabilities, ease of use, and seamless integration with other libraries. Its core data structures, robust handling of missing data, and extensive functionalities for data analysis and transformation make it an indispensable resource for data ...

8. pandas.pydata.org › docs › getting_startedPackage overview — pandas 2.2.2 documentation

   pandas.pydata.org › docs › getting_started

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   pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

9. datagy.io › pandasPython Pandas Tutorial: A Complete Guide • datagy

   datagy.io › pandas

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   Dec 11, 2022 · Why Do You Need Pandas? Pandas is the quintessential data analysis library in Python (and arguable, in other languages, too). It’s flexible, easy to understand, and incredibly powerful.

10. www.freecodecamp.org › news › the-ultimate-guide-toThe Ultimate Guide to the Pandas Library for Data Science in ...

    www.freecodecamp.org › news › the-ultimate-guide-to

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    Jul 8, 2020 · Pandas (which is a portmanteau of "panel data") is one of the most important packages to grasp when you’re starting to learn Python [https://courses.nickmccullum.com/courses/enroll/python-for-finance/]. The package is known for a very useful data structure called the pandas DataFrame.