Essentials for Exploring and Visualizing Data with Pandas#

Here we show some (definitely not all) of what we consider a good starting collection of functionalities from pandas to work with tabular data. Check the pandas user guides to explore more functions and methods.

  1. Data Structures:

    • Series: One-dimensional labeled array.

    • DataFrame: Two-dimensional table of data.

  2. Data Loading:

    • Import data from various sources (CSV, Excel, SQL, JSON) into DataFrames.

  3. Data Cleaning and Preprocessing:

    • Handle missing data using dropna, fillna, and interpolate.

    • Transform data with functions like merge, join, pivot, and melt.

    • Filter data with boolean indexing.

    • Convert data types using astype and specific conversion functions.

  4. Exploratory Data Analysis (EDA):

    • Calculate summary statistics with describe, mean, median, etc.

    • Group and aggregate data using groupby.

    • Sort and rank data with sort_values and rank.

    • Obtain unique values with unique and count occurrences with value_counts.

  5. Data Visualization:

    • Integrate with visualization libraries (Matplotlib, Seaborn, Plotly).

    • Create line plots, bar plots, scatter plots, histograms, etc.

  6. Time Series Data:

    • Utilize DatetimeIndex and time-related functions.

  7. Indexing and Selection:

    • Select data using .loc (label-based) and .iloc (integer-based).

    • Filter rows with boolean indexing.

  8. Data Transformation:

    • Apply functions using apply or applymap.

    • Create new columns based on existing data.

  9. Merging and Joining Data:

    • Combine DataFrames using merge and concat.

  10. Reshaping Data:

    • Pivot, melt, and stack data using pivot, melt, and stack.

  11. Time Series Analysis:

    • Resample, shift, calculate rolling statistics.

  12. Handling Categorical Data:

    • Work with categorical data using Pandas tools.

Remember to practice on real datasets and refer to the Pandas documentation and tutorials for specific tasks.