Data Manipulation in Python: A Pandas Crash Course

Explore finding and loading datasets, saving work, and inspecting data with text and plots. Learn to use kaggle and open sources, load with pandas read_csv.

Open Jupyter notebooks, load data with pandas read_csv to inspect a heart disease dataset, then preview with head and consider encoding, delimiter, and optional pickle speedups.

Compare Pandas and NumPy, focusing on data structures, loading data, and key operations. Learn when to use Pandas, why dot values are deprecated, and how reshaping and copying differ.

Create data frames in pandas using arrays, dictionaries, and structured arrays, set columns and optionally a custom index, then save the frames for later use.

Learn to save and serialize a pandas data frame using csv and pickle formats, tune index and float formatting, and compare performance and file sizes.

Inspect a pandas data frame using head, tail, and sample; then summarize with info, describe, shape, and corr, and inspect value counts for data quality insights.

Explore the basics of data visualization in Python using pandas, matplotlib, and seaborn. Learn bar, scatter, and line plots, group by operations, and plotting fundamentals for distributions and higher dimensions.

Explore how to combine pandas with matplotlib for advanced plotting, integrate seaborn, save figures to PNG or PDF, tune styles, and create multi-axis layouts while avoiding pie charts.

Explore visualizing one-dimensional data with histograms, box plots, and violin plots using Pandas, Matplotlib, and Seaborn, with practical examples on heart disease data.

Explore visualizing two-dimensional distributions with NASA meteorite data using histograms, contour plots, and kernel density estimation, plus 2D joint plots in Python with matplotlib and seaborn.

Explore styling pandas table outputs using df.style to color-code negatives in red, highlight max values in gold, apply color gradients, and render bar plots for data in HTML tables.

Visualize high-dimensional data with scatter matrix and correlation heat maps, then explore manifold learning techniques to reveal nonlinear relationships in heart disease data and synthetic data using pandas and seaborn.

Use histograms for unambiguous, easy-to-digest data, explore 2d and higher-dimensional histograms, and apply correlation matrix plots to reveal relationships; try the Pokemon dataset practice with the attached notebooks.

Mastering indexing, labeling, and sorting in pandas with set_index, reset_index, sort_index, and sort_values, plus unique, value_counts, and rank for data ordering.

Master slicing and filtering in pandas by selecting columns, building boolean masks, and combining conditions with logical operators, while understanding views versus copies and using loc for precise data access.

Learn to replace and threshold data in pandas, handling not a number values with dropna, fillna, and clip, using Airbnb dataset examples.

Explore removing and adding data in pandas dataframes, converting birth dates to datetime, extracting year, using categorical data, dropping or appending rows, inserting and assigning columns, and transposing data.

Learn how to apply, map, and vectorize functions in pandas, compare apply vs map, use vectorized operations, and access string and date time vectorized functions for efficient data manipulation.

Apply boolean masks to filter rows and select the columns you care about, then use dropna or fillna to handle missing values while keeping a copy to avoid mutating data.

Master data grouping and aggregation in pandas by learning groupby syntax, imputation strategies, and custom aggregations to extract precise insights for data scientists and analysts.

Master grouping by store and day of week in pandas, perform aggregations, and visualize the resulting average sales with simple plots.

Apply intelligent imputation in pandas by replacing missing or corrupted values with groupby-based medians and transform-driven fills, preserving data integrity for downstream analysis.

Master grouping and aggregation in pandas, applying single and multiple aggregates via group by and the aggregation function, including mean and other stats.

Discover pandas data manipulation techniques, including multi-level grouping, smart imputation, and aggregation functions, then learn to merge data frames from diverse sources for real-world analysis.

Learn how to merge real-world data using pandas: build basic syntax, concatenate and append data frames, and perform merges and joins to combine tables via keys or indices.

Explore how to merge data in Pandas using inner, left, right, and outer joins, handle duplicates and suffixes, and create cross joins and composite keys.

Explore how to merge time series data with pandas using merge_ordered and merge_as_of, interpolate across cadences, and align temperatures across Australia, US, and Brisbane with forward fill and nearest matching.

Master pandas merging with inner, left, right, and outer joins, including merge_asof for ordered data, load restaurant and user data from CSVs, and identify the top restaurant.

Learn how to create and use multi-indexes in pandas, including cross section and index slice selections, with a focus on efficient lookups using a sorted index and flight data.

Master multi indexes and multi index columns in pandas. Learn creation methods, from set index to from arrays and from product, plus naming and level-based access.

Master stacking and unstacking in pandas to transform data from long format to wide format with a multi-index, control levels, handle missing values, and visualize trends with heatmaps.

Explore pivoting and crosstabs in pandas by transforming election data and world happiness data, showing how to create pivot tables, perform aggregation, and compare countries across years.

Explore pivot margins for rows and columns, customize aggregations with mean, sum, and lambda, and plot grouped data; apply cut to create star ratings.

Explore crosstab in pandas, a wrapper around pivot table that counts occurrences and enables normalization across all, rows, or columns using gender and eye color data.

Explore converting wide frames to long format in Pandas with melt and stack, preserving gender and alignment as IDs, and compare crosstab with pivot_table for counting.

Master advanced Pandas reshaping methods, including pivot, unstack, melt, stack, and pivot tables, with multi-indexing, crosstab, and group by, and prepare for time series data next.

Explore time series data with pandas, learn datetime indexing, slicing, resampling, multi-index handling, and basic visualization using stock market data and timezone tricks in Python.

Master reindexing in pandas for time series data and date ranges. Learn forward, back, and nearest fill methods, and how to handle multi-index securities.

Learn pandas resampling to aggregate data by year or month using resample rules, group by with multi-index, and apply custom functions like mean or median.

Explore rolling functions in pandas using a five-day window to compute rolling means and centered statistics on stock data, and compare boxcar versus gaussian smoothing and alignment.

Discover how pandas localizes and converts time zones to analyze tweet timestamps, comparing Istanbul and Los Angeles times and visualizing hourly patterns with daylight saving considerations.

Review time series concepts in pandas, including datetime index, resampling, partial-date slicing, rolling windows, and imputation, then apply to cryptocurrency data with open, high, low, close, volume, and market cap.

Review how to set up the environment, load data frames, clean and merge, and group data, then work with time series and hierarchical indexes in Pandas.

Learn to customize Jupyter notebooks with the Dupatta dark theme by installing Jupyter extensions and themes, and setting matplotlib defaults via startup scripts.

Explore optional exercises from chapter two to deepen familiarity with data frames in pandas, including loading pickle data and csv formats, whitespace delimiters, and index and header handling.

Explore visual data analysis with pandas and seaborn on a Pokemon dataset, comparing attack and defense, breaking down by type, and examining battle stat distributions.

Apply pandas data manipulation to cost of living data: rename the index to location, split into city and country, find country with the most cities, and sort by housing cost.

Group video game sales data in pandas to identify the genre with the highest average global sales and to compare mean and standard deviation of European sales across genres.

Use pandas to merge restaurant and user data, handle inner joins and duplicates, then group by place id to identify top five restaurants by average rating.

Use pandas to load airline satisfaction data, convert to numeric, create pivot tables of average satisfaction by gender and class, and analyze correlations among online features.

Explore time series analysis of cryptocurrency market data in pandas, including setting a multi-index, identifying top market cap symbols, and plotting closing prices with rolling smoothing.