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Adnan Ahmad has completed

pandas Foundations

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4 hours
5,150 XP
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Course Description

pandas DataFrames are the most widely used in-memory representation of complex data collections within Python. Whether in finance, a scientific field, or data science, familiarity with pandas is essential. This course teaches you to work with real-world datasets containing both string and numeric data, often structured around time series. You will learn powerful analysis, selection, and visualization techniques in this course.
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  1. 1

    Data ingestion & inspection


    In this chapter, you will be introduced to pandas DataFrames. You will use pandas to import and inspect a variety of datasets, ranging from population data obtained from the World Bank to monthly stock data obtained via Yahoo Finance. You will also practice building DataFrames from scratch and become familiar with the intrinsic data visualization capabilities of pandas.

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    Review of pandas DataFrames
    50 xp
    Inspecting your data
    50 xp
    DataFrame data types
    50 xp
    NumPy and pandas working together
    100 xp
    Building DataFrames from scratch
    50 xp
    Zip lists to build a DataFrame
    100 xp
    Labeling your data
    100 xp
    Building DataFrames with broadcasting
    100 xp
    Importing & exporting data
    50 xp
    Reading a flat file
    100 xp
    Delimiters, headers, and extensions
    100 xp
    Plotting with pandas
    50 xp
    Plotting series using pandas
    100 xp
    Plotting DataFrames
    100 xp
  2. 2

    Exploratory data analysis

    Now that you’ve learned how to ingest and inspect your data, you will next learn how to explore it visually and quantitatively. This process, known as exploratory data analysis (EDA), is a crucial component of any data science project. pandas has powerful methods that help with statistical and visual EDA. In this chapter, you will learn how and when to apply these techniques.

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  3. 3

    Time series in pandas

    In this chapter, you will learn how to manipulate and visualize time series data using pandas. You will become familiar with concepts such as upsampling, downsampling, and interpolation. You will practice using method chaining to efficiently filter your data and perform time series analyses. From stock prices to flight timings, time series data can be found in a wide variety of domains, and being able to effectively work with it is an invaluable skill.

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1981-2010 NOAA Austin Climate NormalsJuly 2015 Austin airport departures (Southwest Airlines)Automobile miles per gallonLife expectancy at birth (Gapminder)Stock data (messy)Percentage of bachelor's degrees awarded to women in the USATipsTitanic2010 Austin weatherWorld Bank World Development IndicatorsWorld population


Intermediate Python
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