Pandas is an open-source Python Library that is made mainly for working with relational or labelled data both easily and intuitively. This Python library is built on top of the NumPy library, providing various operations and data structures for manipulating numerical data and time series. Pandas is fast and it has high performance & productivity for users.
This Pandas Exercise is designed for beginners and experienced professionals. In this article, you will learn about all the important Pandas concepts, such as Pandas DataFrames, Pandas DataFrame Rows, Pandas DataFrame Columns and more. These concepts are essential for any budding Data Analyst or Data Scientist.
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List of Pandas Exercises |
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Pandas DataFrame – Practice Exercises
- Make a Pandas DataFrame with a two-dimensional list | PythonÂ
- Python | Creating DataFrame from dict of narray/listsÂ
- Python | Creating DataFrame from dict of narray/listsÂ
- Creating Pandas dataframe using list of listsÂ
- Creating a Pandas dataframe using list of tuplesÂ
- Create a Pandas DataFrame from List of DictsÂ
- Python | Convert list of nested dictionary into Pandas dataframeÂ
- Replace values in Pandas dataframe using regexÂ
- Creating a dataframe from Pandas seriesÂ
- Construct a DataFrame in Pandas using string dataÂ
- Clean the string data in the given Pandas DataframeÂ
- Reindexing in Pandas DataFrameÂ
- Mapping external values to dataframe values in PandasÂ
- Reshape a pandas DataFrame using stack, unstack and melt methodÂ
- Reset Index in Pandas DataframeÂ
- Python | Change column names and row indexes in Pandas DataFrameÂ
Pandas Dataframe Rows – Practice ExerciseÂ
- How to iterate over rows in Pandas DataframeÂ
- Different ways to iterate over rows in Pandas DataframeÂ
- Selecting rows in pandas DataFrame based on conditionsÂ
- Select any row from a Dataframe using iloc[] and iat[] in PandasÂ
- Limited rows selection with given column in Pandas | PythonÂ
- Drop rows from the dataframe based on certain condition applied on a columnÂ
- Insert row at given position in Pandas DataframeÂ
- Create a list from rows in Pandas dataframeÂ
- Create a list from rows in Pandas DataFrame | Set 2Â
- Ranking Rows of Pandas DataFrameÂ
- Sorting rows in pandas DataFrameÂ
- Select row with maximum and minimum value in Pandas dataframeÂ
- Get all rows in a Pandas DataFrame containing given substringÂ
- Convert a column to row name/index in PandasÂ
- How to randomly select rows from Pandas DataFrameÂ
Pandas Dataframe Columns – Practice Exercise
- Create a pandas column using for loopÂ
- How to get column names in Pandas dataframeÂ
- How to rename columns in Pandas DataFrameÂ
- Collapse multiple Columns in PandasÂ
- Get unique values from a column in Pandas DataFrameÂ
- Conditional operation on Pandas DataFrame columnsÂ
- Return the Index label if some condition is satisfied over a column in Pandas DataframeÂ
- Using dictionary to remap values in Pandas DataFrame columnsÂ
- Formatting integer column of Dataframe in PandasÂ
- Create a new column in Pandas DataFrame based on the existing columnsÂ
- Python | Creating a Pandas dataframe column based on a given conditionÂ
- Split a column in Pandas dataframe and get part of itÂ
- Getting Unique values from a column in Pandas dataframeÂ
- Split a String into columns using regex in pandas DataFrameÂ
- Getting frequency counts of a columns in Pandas DataFrameÂ
- Change Data Type for one or more columns in Pandas DataframeÂ
- Split a text column into two columns in Pandas DataFrame Â
- Difference of two columns in Pandas dataframeÂ
- Get the index of maximum value in DataFrame columnÂ
- Get the index of minimum value in DataFrame columnÂ
- Get n-largest values from a particular column in Pandas DataFrameÂ
- Get n-smallest values from a particular column in Pandas DataFrameÂ
- How to drop one or multiple columns in Pandas DataframeÂ
- How to lowercase column names in Pandas dataframeÂ
- Capitalize first letter of a column in Pandas dataframeÂ
- Apply uppercase to a column in Pandas dataframe Â
Pandas Series – Practice Exercise
- Create a Pandas Series from arrayÂ
- Creating a Pandas Series from DictionaryÂ
- Creating a Pandas Series from ListsÂ
- Create Pandas Series using NumPy functionsÂ
- Access the elements of a Series in Pandas Â
Pandas Date and Time – Practice Exercise
- Pandas | Basic of Time Series ManipulationÂ
- Using Timedelta and Period to create DateTime based indexes in PandasÂ
- Convert the column type from string to datetime format in Pandas dataframe Â
FAQs on Pandas Exercise
Q1: Why do people use Pandas in Python?
Pandas are basically used in Python for data manipulation, analysis, and cleaning tasks. It provides Tabular data structures and In-built functions that simplify data handling and allow for efficient data processing.
Q2: Where can I practice Python Pandas?
There are several online platforms where you can practice Python Pandas, such as DataCamp, Kaggle, and HackerRank. Additionally, you can find exercises and tutorials on websites like Lazyroar and Real Python.
Q3: What are the benefits of Pandas?
Here are some  Benefits that Pandas Offer :-
- Data manipulation: Pandas provides efficient data structures like DataFrames, which allow for easy manipulation, filtering, and transformation of data.
- Data analysis: It offers a wide range of In-built functions for exploratory data analysis and other statistical analysis.
- Integration: Pandas integrates well with other libraries in the Python ecosystem, such as NumPy, Matplotlib, and Scikit-learn, enabling seamless data analysis and visualization workflows.
- Flexibility: Pandas supports various file formats, including CSV, Excel, SQL databases, and more, making it versatile for data ingestion and export.
Q4: What are the features of Pandas?
Here are some key features of Pandas are:
- DataFrame: A two-dimensional labelled Tabular(table-like) data structure that provides columnar data manipulation capabilities.
- Data cleaning and preparation: Pandas offers functions for handling missing data, data transformation, and data normalization.
- Data aggregation and summarization: It provides methods for grouping data, computing summary statistics, and applying custom functions.
- Time series analysis: Pandas has built-in support for handling time series data, including date/time indexing, resampling, and window functions.
- Input/output tools: It supports reading and writing data in various formats, such as CSV, Excel, SQL databases, and more.
- Visualization: Pandas integrates well with Matplotlib for data visualization and provides convenient plotting functions.
