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.