Pandas cheat sheet

1. 1Loading and saving dataThe two objects pandas is built on, how to read CSV, Excel, SQL and Parquet into them, and how to write results back out.6 concepts

   1. 1DataFrame and SeriesA Series is one labelled column of values with a single dtype.
   2. 2read_csvpd.read_csv reads a delimited text file into a DataFrame, guessing column types.
   3. 3Excel, SQL, JSON and Parquetpandas has a reader for each common source: read_excel for workbooks, read_sql for a database query, read_json for JSON and read_parquet for columnar…
   4. 4Writing data outEvery reader has a writer: to_csv, to_excel, to_parquet, to_sql.
   5. 5Why Parquet over CSVParquet is a compressed, columnar file format that stores each column's type alongside the data.
   6. 6First look at a datasethead() shows the first rows, shape the row and column count, info() each column's dtype and non-null count, and describe() summary statistics.

   6 in this chapterOpen this chapter
2. 2Selecting rows and columnsPicking columns, the difference between loc and iloc, the index, and how copy-on-write changes what happens when you modify a selection.6 concepts

   1. 1Selecting columnsSquare brackets with one name return a Series; with a list of names they return a DataFrame with those columns in that order.
   2. 2loc: select by labeldf.loc[rows, cols] selects by index labels and column names, and accepts boolean masks.
   3. 3iloc: select by positiondf.iloc[rows, cols] selects by integer position, like Python lists: the end of a slice is excluded.
   4. 4The indexThe index is the set of row labels.
   5. 5Copy-on-writeSince pandas 3.0 every selection or method result behaves as a new object: changing it never changes the original. pandas only copies data when you actually…
   6. 6Sampling rowssample(n) or sample(frac=0.1) picks random rows; nlargest and nsmallest return the top or bottom rows by a column.

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3. Filtering and sortingBoolean masks, combining conditions, query, membership tests and sorting.6 conceptsOpens with credits
4. Cleaning and missing valuesFinding and handling missing values, fixing types, removing duplicates and renaming columns.7 conceptsOpens with credits
5. New columns and transformationsCreating columns with vectorised maths, assign, pd.col, conditional logic, binning, map and apply, and text and category helpers.7 conceptsOpens with credits
6. Grouping and aggregatingSplit, apply, combine: groupby with named aggregations, transform, counting, pivot tables and crosstabs.7 conceptsOpens with credits
7. Merge, join and concatCombining tables side by side on keys, stacking them on top of each other, and checking that a join did what you expected.6 conceptsOpens with credits
8. ReshapingSwitching between long and wide tables with melt, pivot, stack and unstack, and expanding list columns.5 conceptsOpens with credits
9. Dates and time seriesParsing dates, the .dt accessor, resampling, rolling windows, shifts and time zones.7 conceptsOpens with credits
10. Performance and good habitsKeeping pandas fast and memory-light, and writing analysis code others can rerun.6 conceptsOpens with credits
11. 11Glossary, A to ZEvery concept in the sheet and the smaller terms it uses, in one list.74 terms

Reading data, selecting, filtering, missing values, groupby, merge and join, reshaping and dates in pandas, with copy-on-write in mind.