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pandas provides Python data structures and analysis tools for exploring, cleaning, and processing tabular data such as spreadsheets and database tables. Its main table structure is the DataFrame. This guide focuses on choosing a command for the task rather than listing every method or option.
How to use this pandas cheat sheet
Start with the operation you need, then consult the official user guide for concepts and the API reference for exact signatures and parameters. The API reference assumes you already understand the underlying concepts, while the user guide explains them with examples. New to pandas? Begin with 10 minutes to pandas.
How do I read a CSV with pandas?
Use read_csv() to load a CSV file into a DataFrame. To save the result as a CSV, use to_csv():
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import pandas as pd
df = pd.read_csv("sales.csv")
df.to_csv("sales_clean.csv", index=False)index=False prevents the DataFrame’s row labels from being written as an extra CSV column. pandas also provides read_* functions and to_* methods for formats such as Excel, SQL, JSON, and Parquet. Options vary by format; see the IO tools guide for supported formats and details.
How do I inspect a DataFrame?
Check a few rows and the structure before transforming data. These commands show a sample, column names and types, and a compact summary of numeric columns:
df.head()
df.info()
df.describe()For a quick size check, use df.shape; it returns a pair of values in the order rows, columns. Inspection helps reveal unexpected column types or missing values before calculations depend on them.
How do I select rows and columns?
Use [] for a column or a Boolean row filter, .loc to select by labels, and .iloc to select by integer positions. Label-based selection and position-based selection are different; choose according to what identifies the rows and columns you want.
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# One column; result is a Series
amounts = df["amount"]
# Rows matching a condition
large_sales = df[df["amount"] > 100]
# Select rows and columns by labels
subset = df.loc[df["region"] == "West", ["region", "amount"]]
# Select by integer positions: first five rows, first two columns
sample = df.iloc[:5, :2]Use the indexing and selecting data guide for slicing, alignment, and less common selection cases.
How do I clean and transform data?
Create a derived column
Column operations work across values without requiring an explicit Python loop over rows. For example, calculate a line total from price and quantity:
df["line_total"] = df["price"] * df["quantity"]Handle missing values
Use isna() to identify missing values. Then decide whether to remove affected rows or fill values; that choice depends on what the missing entries mean in your data.
missing_by_column = df.isna().sum()
complete_rows = df.dropna()
filled = df.fillna({"quantity": 0})Filling a missing value with zero is appropriate only when zero is a valid substitute for the missing value in that field. See the missing data guide for additional behavior and options.
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Remove duplicate rows
drop_duplicates() removes duplicate rows. Pass a column name or list of column names to define which values determine duplication:
unique_records = df.drop_duplicates()
unique_customers = df.drop_duplicates(subset="customer_id")Clean text values
For string columns, the .str accessor provides vectorized text operations. This example trims leading and trailing whitespace:
df["name"] = df["name"].str.strip()For pandas 3.0, string behavior and the new string data type are version-sensitive. If you maintain code written for earlier releases, check the pandas 3.0 string migration guide rather than assuming behavior is unchanged.
How do I calculate summaries and group data?
For a whole-column summary, use methods such as mean(), sum(), or value_counts(), depending on the question. To calculate summaries for each category, use groupby(): it splits rows into groups, applies a calculation, and combines the results.
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# Overall average
average_amount = df["amount"].mean()
# Total amount for each region
sales_by_region = df.groupby("region")["amount"].sum()
# Several summaries for each region
summary = df.groupby("region").agg(
total_amount=("amount", "sum"),
average_amount=("amount", "mean"),
order_count=("amount", "count"),
)Use agg() when a group needs multiple summaries. The groupby guide covers split-apply-combine, custom aggregations, and related patterns; use the windowing guide when the calculation needs a rolling or other window over observations.
How do I reshape wide data to long format?
Use melt() when several measurement columns should become rows. Use pivot() to move long-form values into columns. A pivot expects the chosen index-and-column combinations to identify single values; when duplicates require aggregation, use pivot_table() instead.
# Wide: one row per person, with separate year columns
wide = pd.DataFrame({
"person": ["Ari", "Bo"],
"sales_2025": [12, 9],
"sales_2026": [15, 11],
})
# Long: year and sales become values in two columns
long = wide.melt(
id_vars="person",
var_name="year",
value_name="sales",
)
# Wide again, using a unique person/year pair
restored = long.pivot(index="person", columns="year", values="sales")Read the reshaping and pivot tables guide for multi-index and aggregation cases.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I combine two DataFrames?
Choose the combining method based on how the tables relate. concat() stacks tables along an axis; merge() matches rows using key columns, like a database join; join() is commonly used to combine on indexes.
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# Stack rows from similarly structured tables
all_months = pd.concat([january, february], ignore_index=True)
# Match customer records using a shared key
combined = orders.merge(customers, on="customer_id", how="left")Before relying on a merge, check that the key columns mean the same thing in both tables and that their values have compatible types. Then inspect the resulting row count and unmatched records: duplicate keys can multiply rows, while join type affects which unmatched rows remain. See the merging, joining, and concatenating guide.
How do I work with dates and times?
Parse a date column during CSV import with parse_dates, or convert it afterward with to_datetime(). Once a column contains datetime values, the .dt accessor can extract date components:
df = pd.read_csv("sales.csv", parse_dates=["order_date"])
df["order_year"] = df["order_date"].dt.yearFor time-based indexing, resampling, and time-series-specific behavior, use the time series guide.
Where should I look for more pandas help?
- 10 minutes to pandas is the recommended starting point for new users.
- The pandas 3.0.6 User Guide explains topics such as indexing, IO, missing data, grouping, reshaping, text, time series, and scaling.
- The API reference is the place to check exact function and method parameters.
- The official project recommends Python for Data Analysis by Wes McKinney as a book for learning pandas; see the project getting-started page.
For large datasets, consult the user guide’s scaling to large datasets section. It discusses approaches including loading less data, using efficient data types, and chunking; pandas is not a reason to ignore memory constraints.
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