Tags

plotting

Pandas allows us to create a number of different plot types, using an API that wraps around Matplotlib. We can create line, bar, pie, scatter, and box plots, among others.

241 issues

grouping

Using Pandas groupby to split data into groups, apply aggregate functions, and combine results for powerful data summarization.

167 issues

datetime

Pandas offers a wide set of functionality having to do with dates and times, from extracting values to calculating the differences between dates, to time series, to resampling.

139 issues

csv

Reading from and writing to CSV files with Pandas, including options for delimiters, encoding, headers, and handling large files.

107 issues

pivot-table

A pivot table redisplays information from a data frame in a new way: It uses the unique values from one column as its index, the unique values from a second column for its columns, and then applies an aggregation function to all of the values in each row-column intersection.

103 issues

excel

Reading from and writing to Excel spreadsheets with Pandas, including working with multiple sheets, formatting, and large workbooks.

101 issues

window-functions

Window functions perform aggregate operations across either a growing number of rows or a "sliding window" of rows.

82 issues

joins

Joining data frames together with "join" and "merge"

77 issues

strings

Working with text data in Pandas using the str accessor for operations like splitting, stripping, case conversion, and pattern matching.

71 issues

cleaning

Techniques for cleaning messy data in Pandas, including handling duplicates, fixing data types, correcting errors, and standardizing values.

68 issues

multi-index

In Pandas, the rows and columns are labeled with an index. But each of them can have more than one dimension, known as a "multi-index."

65 issues

multiple-files

Loading and combining data from multiple files into a single Pandas data frame using concat, glob patterns, and loops.

58 issues

plotly

Creating interactive, web-based visualizations from Pandas data using Plotly and Plotly Express.

57 issues

regular-expressions

Using regular expressions with Pandas string methods to search, extract, replace, and validate text patterns in data.

57 issues

filtering

Selecting subsets of data in Pandas using boolean indexing, loc, iloc, query, and other filtering techniques.

54 issues

web-scraping

Extracting data from websites and HTML pages into Pandas data frames using tools like read_html, Beautiful Soup, and requests.

35 issues

correlations

Calculating and interpreting correlations between columns in Pandas, using methods like Pearson, Spearman, and Kendall.

30 issues

sorting

Sorting data frames and series in Pandas by values or index, including multi-column sorts and custom sort orders.

27 issues

api

Retrieving data from web APIs and loading it into Pandas data frames for analysis, including REST APIs and public data sources.

25 issues

pyarrow

Using PyArrow as a backend for Pandas to improve performance, memory efficiency, and support for additional data types.

24 issues

memory-optimization

Techniques for reducing memory usage in Pandas, including choosing efficient dtypes, chunked reading, and categorical data.

24 issues

office-hours

Questions and answers from Bamboo Weekly office hours sessions, covering a variety of Pandas topics raised by subscribers.

23 issues

stack-unstack

Reshaping data frames using Pandas stack and unstack to pivot between long and wide formats via multi-level indexes.

21 issues

pipe

Using the Pandas pipe method to chain custom functions into data processing pipelines for cleaner, more modular code.

20 issues

formatting

Controlling how Pandas displays data, including number formatting, column widths, decimal places, and string representation of data frames.

18 issues

comprehensions

Using Python list, dict, and set comprehensions to create and transform data efficiently alongside Pandas.

18 issues

apply-function

Using Pandas' apply method to run custom functions across rows, columns, or entire data frames for flexible data transformations.

16 issues

missing-data

Detecting, handling, and filling missing (NaN/None) values in Pandas using methods like fillna, dropna, and isna.

16 issues

json

Reading, writing, and normalizing JSON data with Pandas, including handling nested structures and JSON lines format.

12 issues

index-operations

Working with Pandas indexes, including setting, resetting, reindexing, and manipulating row and column labels.

12 issues

speed-optimization

Techniques for making Pandas code run faster, including vectorization, avoiding loops, and leveraging optimized operations.

12 issues

seaborn

Creating statistical visualizations from Pandas data using Seaborn, a high-level plotting library built on Matplotlib.

11 issues

interpolation

Filling in missing values in Pandas using interpolation methods such as linear, polynomial, and time-based techniques.

8 issues

geopandas

Working with geospatial data using GeoPandas, which extends Pandas with support for geographic operations, shapefiles, and spatial joins.

8 issues

styling

Applying visual styles to Pandas data frames using the Styler API, including conditional formatting, color maps, and bar charts in cells.

8 issues

text

Reading and processing plain text files and text data with Pandas, including parsing, tokenizing, and text analysis.

6 issues

polars

Using Polars, a fast DataFrame library written in Rust, as an alternative or complement to Pandas for high-performance data analysis.

6 issues

renaming-columns

Renaming columns in Pandas data frames using rename, set_axis, and direct assignment for clearer, more consistent column names.

4 issues

pdf-extraction

Extracting tables and data from PDF files and loading them into Pandas data frames using libraries like tabula-py and camelot.

4 issues

marimo

Using Marimo, a reactive Python notebook, as an alternative to Jupyter for interactive data exploration with Pandas.

4 issues

where

Using the Pandas where and mask methods to conditionally replace values in a data frame based on boolean conditions.

2 issues

fireducks

Using FireDucks, a high-performance drop-in replacement for Pandas that accelerates data frame operations.

2 issues

xarray

Using xarray for labeled, multi-dimensional arrays that extend Pandas-like functionality to higher-dimensional data.

2 issues

parquet

Reading and writing Parquet files with Pandas, a columnar storage format that offers fast I/O and efficient compression for large datasets.

2 issues

ai-queries

Using AI and large language models to query, analyze, and transform data in Pandas data frames.

2 issues

duckdb

Using DuckDB, an in-process analytical database, alongside Pandas for fast SQL-based querying of data frames and files.

2 issues

maps

Creating geographic maps and visualizations from Pandas data using libraries like Folium, Plotly, and GeoPandas.

2 issues

yaml

Reading and parsing YAML configuration and data files into Python structures for use with Pandas.

2 issues

agentic-coding

2 issues

cutting

Using Pandas cut and qcut to bin continuous data into discrete intervals or quantile-based buckets.

2 issues

outlier-detection

Identifying and handling outliers in Pandas data using statistical methods like z-scores, IQR, and visualization techniques.

2 issues

functions

Writing and using Python functions to organize and streamline data analysis workflows in Pandas.

2 issues

fixed-width-fields

Reading and parsing fixed-width text files with Pandas using read_fwf, where columns are defined by character positions rather than delimiters.

2 issues

chatbot

Building chatbots and conversational interfaces that use Pandas for data retrieval and analysis.

2 issues

dummy-values

Creating dummy (indicator) variables from categorical data using Pandas get_dummies for use in statistical modeling and machine learning.

2 issues

regression

Performing linear and other regression analyses on Pandas data to model relationships between variables and make predictions.

2 issues

encoding

Handling text encoding issues when reading and writing data in Pandas, including UTF-8, Latin-1, and other character encodings.

2 issues

sas

Reading SAS data files (.sas7bdat, .xpt) into Pandas and transitioning from SAS workflows to Python-based data analysis.

2 issues

method-chaining

Chaining multiple Pandas methods together in a single expression for cleaner, more readable data transformation pipelines.

2 issues

crosstabs

Using Pandas crosstab to compute cross-tabulations of two or more factors, summarizing relationships between categorical variables.

2 issues

great-tables

Using the Great Tables library to create beautiful, publication-quality tables from Pandas data frames.

2 issues

streamlit

Building interactive data apps and dashboards with Streamlit, powered by Pandas data frames.

2 issues

timing

Measuring and benchmarking the execution time of Pandas operations to identify bottlenecks and compare approaches.

1 issue