The Ultimate Python Library Pandas Training Course

Install pandas via the Anaconda distribution and conda install pandas, then launch Jupyter Notebook. Learn edit mode and command mode, and common cell shortcuts for creating, running, and deleting cells.

Understand why pandas is an open-source, general-purpose data tool in Python, and how it contrasts with R, Excel, and MATLAB, while highlighting notebooks that document your analysis.

Explore series in Pandas and compare them to Python lists by calculating the mean of a temperatures list. Learn about list comprehension filtering, docstrings, and Jupyter tips.

Explore numpy basics, including importing as np and the fast array object built on C and Fortran. Compare numpy to Python, using boolean filtering and built in methods like mean.

Learn to create a pandas series, import pandas as pd, set a name, inspect index and dtype, and compute mean and sum, highlighting database-like operations.

Learn how to create boolean masks in pandas, filter a series with true values, apply and/or/not with correct precedence, and understand index and date range concepts for time series analysis.

See how pandas series store diverse data types, from int64 and float64 to object. Convert date-like strings with to_datetime to datetime64 and use category type for efficient machine learning data.

Create a series from a Python list and loop values. Containment tests check the index, not values; use s.values or set(s) for membership and s.items() in Python 3.

Explore broadcasting in NumPy and pandas, where adding a scalar or another series aligns by index and yields NaN where overlaps exist.

Explore CRUD operations on a Pandas series, focusing on reading by index and label with iloc and loc, handling integer versus non-integer indices, and using transpose with T.

Explore how to update and delete a pandas Series using label and position-based assignments, in-place mutations, and boolean masking, including del, set value, and filtering techniques.

Explore summary statistics on a pandas series using mean, max, median, and describe. Learn how value_counts summarizes categorical data and how percentiles and quantiles customize describe results.

This lecture shows how pandas series handle duplicates using duplicated and masks, explores keep options (first, last, false), and contrasts iloc versus loc with duplicate indices.

Explore how pandas handles missing values with NaN, None, and object types in series, including isnull, any, all, dropna, and fill methods with forward and backfill options.

Learn to plot a pandas series in Jupyter with a plotting library, using inline plots, importing plt, and choosing bar, histogram, box, or pie plots while customizing axis labels.

Learn to read and write csv files with pandas, control headers and index labels, serialize a series to csv, and convert between series and data frames using read_csv and from_csv.

Learn to create and inspect data frames in pandas, build columns from dictionaries, access via dot or index axis, and explore with info and describe.

Explore how pandas dataframes resemble dictionaries, create and align new columns by index, broadcast scalars, access columns by attribute or key, and delete with del or pop.

Create a pandas DataFrame from a dictionary or a list of dictionaries, from JSON data, and use the DataFrame constructor with read_json and json.dumps along with the columns parameter.

Learn to create a dataframe from csv files using pandas read_csv in a jupyter notebook. Pass a file path, and rely on read_csv to handle well-formatted csv with minimal options.

Explore how pandas performs selection and filtering using boolean masks, string operations, and query syntax, aligning with relational algebra concepts and SQL-like data manipulation.

Learn projection in pandas by pulling columns from a dataframe using dot and index access, returning series or dataframes, and using masking and slicing to filter by name or position.

Learn how pandas uses merge for product joins, including inner, outer, left, and right joins, plus union with concat and append, difference with masks and index considerations.

Explore pandas data frames by loading a 1880 social security baby names dataset, cleaning headers, converting gender to category to save memory, and inspecting with head, tail, shape, and axes.

Explore data frames by examining indexes and columns, using index, unique, duplicates, describe, value counts, and simple group by operations to summarize numeric and categorical data.

Explore data distributions by creating histograms and box plots to reveal frequency and outliers, then transpose dataframes to inspect and slice data.

Learn how to add rows to a dataframe using append, concat, and iloc or loc, illustrated with Steph Curry stats and a step-by-step dataframe construction.

Learn how to add and manipulate columns in pandas dataframes using index assignment, attribute access, and vectorized operations; apply functions to compute percentages and remove or rename columns.

Learn to sort a pandas dataframe with sort_values and sort_index, adjust ascending and not a number position, and sort by multiple columns using axis zero or axis one.

Explore how dataframes are iterated: by default over column names, using items to access (name, series), iterrows and itertuples for row-wise iteration, and iterating rows as a series.

Learn how to update individual cells in a pandas data frame using set_value, iloc, and loc, including mutating in place, adding new rows or columns, and handling missing values.

Learn how to concatenate data frames in pandas, align rows or columns with outer and inner joins, handle duplicates with verify integrity, and reset indices with ignore_index.

Perform inner and outer joins in pandas with merge, controlling keys via left on and right on, using indicators and suffixes to clarify merged results.

Learn to perform left and right joins in pandas using merge, handle missing values with nan, use an indicator, and merge on color or car color with optional sorting.

Learn how to perform index joins in pandas with merge using left_index or right_index, handle column name overlaps with suffixes, and compare join versus merge for joins.

Explore filtering dataframes by selecting columns and rows using masks, boolean arrays, and string methods, while loading pipe-delimited csv data with a string io buffer and read_table.

Learn to filter dataframes in pandas by building boolean masks and applying predicate functions, including custom is_prime logic, and compare with built-in vectorized operations for cleaner, faster filtering.

Learn to filter dataframes with boolean masks, combine conditions using intersection and union, and manage missing data with isnull, dropna, and fillna in pandas.

Learn to group data by teacher and age in pandas, describe aggregated results, and apply functions to compute mean, median, max, and other summaries.

Learn to create pivot tables in pandas, using pivot and pivot_table with index and columns, handling duplicates, and applying agg function for mean, max, and length with margins.

Master pandas stacking by turning multi-index pivot columns into rows and flattening data, using stack and unstack to make data taller or flatter for flexible data ingestion and plotting.

Learn how pandas handles CSV IO by writing and reading data frames with to_csv and read_csv, addressing encoding, index options, parsing dates, and missing headers.

Learn how pandas serializes and reads json with to_json and read_json, converts dates with convert_dates and to_datetime, and exports to excel using to_excel and read_excel.

Learn to plot with pandas by importing libraries and data, enable inline matplotlib plots, and customize axes, indexing, and x tick labels for clearer charts.

Learn to plot histograms with pandas, adjust layouts with matplotlib's tight layout, customize bins and colors, and interpret cumulative and normalized histograms to understand data distribution.

Learn to create bar plots from a pandas dataframe, filter columns, and switch between vertical and horizontal bars using built-in pandas plotting.

Explore line plots in pandas, plotting dataframes and series, customize axes, labels, rotation, colors, line styles, markers, and limits to visualize trends like song productivity across years.

Explore plotting with pandas and matplotlib to create histograms, box plots, and scatter matrices from a csv dataset. Tweak layouts and filter columns for year and position insights.

Explore dealing with time series in pandas by loading a csv, coercing date columns, and using date parsing, errors handling, and datetime attributes to extract year, month, and more.

Explore window functions and time series in pandas, including creating date ranges, shifting series, resampling to means, and computing rolling fifty-day and hundred-day averages to analyze golden cross signals.

Explore plotting time series in pandas by displaying the S&P 500 close with the 50- and 200-day moving averages, illustrating the golden cross as a buy or sell signal.

Learn to perform classification and regression on iris with pandas and a random forest model. Create X and Y, encode the class with get_dummies, train with fit, and predict.

Learn to crawl data from the Utah Avalanche Center fatalities page, extract key-value pairs using Beautiful Soup and requests, and clean and organize the data with pandas.

Learn how to crawl HTML pages with pandas using the request library and BeautifulSoup. Extract field labels and items, build dictionaries, convert to a dataframe, and save to CSV.

Learn how pandas munge and clean data, inspect data types, and use value counts for categorical fields, while converting depth from feet and inches to inches and loading csv data.

Learn pandas data munging: convert occurrence dates to datetime and extract year and date of week, and use lambdas to parse coordinates into latitude and longitude.

Learn how to plot data with pandas, create histograms and scatter plots, filter outliers, and build interactive maps with Google Maps and OpenStreetMap-based tools in a Jupyter notebook.

Create vector-ready svg graphics from Python plots using seaborn and matplotlib, exporting figures for vector editors like Inkscape, Illustrator, or Sketch, and visualize deaths, locations, triggers, elevations, slopes, and aspects.

Explore the pandas library, including series and dataframe structures, data tweaking, and plotting, and apply pandas to machine learning and analysis, with a Utah avalanche data project in Python.