Python - Introduction to Pandas and DataFrames

Python 3    |    Intermediate
  • 14 videos | 1h 4m 54s
  • Includes Assessment
  • Earns a Badge
Likes 303 Likes 303
Simplify data analysis with Pandas DataFrames. Pandas is a Python library that enables you to work with series and tabular data, including initialization, and population. For this course, learners do not need prior experience working with Pandas, but should be familiar with Python3, and Jupyter Notebooks. Topics include the following: Define your own index for a Pandas series object; load data from a CSV (comma separated values) file, to create a Pandas DataFrame; Add and remove data from your Pandas DataFrame; Analyze a portion of your DataFrame; Examine how to reshape or reorient data, and to create a pivot table. Finally, represent multidimensional data in two-dimensional DataFrames, with multi or hierarchical indexes.

WHAT YOU WILL LEARN

  • understand the various applications of Pandas and why it is a building block in the field of data science
    install Pandas and create a Pandas Series
    work with Pandas Series by accessing elements using the default and a custom index
    define a Pandas DataFrame and describe how data can be stored and accessed in these data structures
    initialize and populate a simple Pandas DataFrame
    load data into a DataFrame from a CSV file
    edit individual cells and entire rows and columns in a Pandas DataFrame
  • access specific rows and columns of a Pandas DataFrame using the index and labels
    access parts of a Pandas DataFrame based on specific conditions
    describe the concept of a hierarchical index or multi-index and how it can be useful
    re-orient a DataFrame as a pivot table to better visualize data
    apply a multi-index to a DataFrame and reshape it using the stack and melt operations
    work with Pandas for basic tabular data manipulation

IN THIS COURSE

  • 2m 16s
  • 3m 2s
    Upon completion of this video, you will be able to understand the various applications of Pandas and why it is a building block in the field of data science. FREE ACCESS
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    3.  Introduction to Pandas
    5m 39s
    To install Pandas, follow these instructions. To create a Pandas Series, use this code. FREE ACCESS
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    4.  Work with Pandas
    6m 11s
    In this video, learn how to work with Pandas Series by accessing elements using the default index and a custom index. FREE ACCESS
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    5.  Introduction to DataFrames
    2m 24s
    In this video, you will define a Pandas DataFrame and describe how data can be stored and accessed in these data structures. FREE ACCESS
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    6.  Work with DataFrames
    5m 56s
    Learn how to initialize and populate a simple Pandas DataFrame. FREE ACCESS
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    7.  Load Data into a DataFrame
    2m 51s
    In this video, you will learn how to load data into a DataFrame from a comma-separated values file. FREE ACCESS
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    8.  Add and Delete DataFrame Contents
    7m 58s
    Learn how to edit individual cells and entire rows and columns in a Pandas DataFrame. FREE ACCESS
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    9.  Select Parts of a DataFrame
    5m 19s
    In this video, you will learn how to access specific rows and columns of a Pandas DataFrame using the index and labels. FREE ACCESS
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    10.  Access Pandas DataFrames
    5m 20s
    In this video, you will learn how to access parts of a Pandas DataFrame based on specific conditions. FREE ACCESS
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    11.  Introduction to Multi-Indexing in a Dataframe
    2m 58s
    After completing this video, you will be able to describe the concept of a hierarchical index or multi-index and why it can be useful. FREE ACCESS
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    12.  Reshape DataFrames
    4m 19s
    Learn how to reorient a DataFrame as a pivot table to better visualize data. FREE ACCESS
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    13.  Reshape Dataframes Using Stack and Melt Operations
    6m 28s
    In this video, you will apply a multi-index to a DataFrame and reshape it using the stack and melt operations. FREE ACCESS
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    14.  Exercise: Pandas for Basic Tabular Data Manipulation
    4m 12s
    Learn how to work with Pandas for basic tabular data manipulation. FREE ACCESS

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