pandas Cheatsheet

Series and DataFrame

Use this pandas reference while you build software engineering projects, review code for technical interview prep, or polish examples for a software engineer resume.

Setup and Core Objects

import pandas as pd
import numpy as np

pd.__version__                    # check installed version

# Display options
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", 100)
pd.set_option("display.float_format", "{:.2f}".format)

Object	Description
`Series`	1-D labeled array: values + `index`
`DataFrame`	2-D labeled table: columns of Series sharing a row index
`Index`	Immutable axis labels (rows or columns)

Creating a Series

import pandas as pd
import numpy as np

# From list
s = pd.Series([10, 20, 30])

# From list with custom index
s = pd.Series([10, 20, 30], index=["a", "b", "c"])

# From dict (keys become index)
s = pd.Series({"a": 10, "b": 20, "c": 30})

# From scalar (broadcast)
s = pd.Series(5, index=["a", "b", "c"])

# With name
s = pd.Series([1, 2, 3], name="score")

Series Attributes

Attribute	Description
`s.values`	Underlying NumPy array (or ExtensionArray)
`s.index`	Index object
`s.dtype`	Data type
`s.name`	Series name
`s.shape`	Tuple `(n,)`
`s.size`	Total number of elements
`s.nbytes`	Memory usage in bytes
`s.is_monotonic_increasing`	True if values are monotonically increasing

Creating a DataFrame

# From dict of lists (keys = column names)
df = pd.DataFrame({"name": ["Alice", "Bob"], "age": [30, 25]})

# From list of dicts (each dict = one row)
df = pd.DataFrame([{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}])

# From 2-D NumPy array
df = pd.DataFrame(np.arange(12).reshape(3, 4), columns=["a", "b", "c", "d"])

# From another DataFrame (copy)
df2 = pd.DataFrame(df)

# With explicit index
df = pd.DataFrame({"x": [1, 2, 3]}, index=["r1", "r2", "r3"])

# Empty DataFrame with schema
df = pd.DataFrame(columns=["id", "value"], dtype=float)

DataFrame Attributes

Attribute	Description
`df.shape`	`(rows, cols)`
`df.dtypes`	Per-column dtype Series
`df.columns`	Column Index
`df.index`	Row Index
`df.values`	Underlying 2-D NumPy array
`df.size`	Total element count
`df.ndim`	Always `2`
`df.empty`	`True` if any axis is length 0
`df.axes`	List `[row_index, col_index]`
`df.memory_usage(deep=True)`	Bytes per column

Inspecting a DataFrame

df.head(5)         # first N rows (default 5)
df.tail(5)         # last N rows
df.sample(10)      # random N rows
df.info()          # column dtypes + non-null counts
df.describe()      # stats for numeric cols
df.describe(include="all")  # stats for all cols
df.nunique()       # unique value count per column
df.value_counts()  # (on Series) frequency table

Index Operations

# Reset index (moves index to a column)
df.reset_index()
df.reset_index(drop=True)     # discard old index

# Set a column as index
df.set_index("name")
df.set_index(["year", "month"])   # MultiIndex

# Rename index
df.index.rename("row_id", inplace=True)

# Re-index to new label set (introduces NaN for missing)
df.reindex([0, 1, 2, 99])

# Sort by index
df.sort_index()
df.sort_index(ascending=False)

Copying and Casting

df_copy = df.copy()             # deep copy (default)
df_copy = df.copy(deep=False)   # shallow copy

# Cast a column
df["age"] = df["age"].astype(int)
df = df.astype({"age": int, "score": float})

# Convert Series dtype
s = s.astype("category")
s = s.astype("string")   # pd.StringDtype (nullable)

Converting Between Series and DataFrame

# Series → single-column DataFrame
s.to_frame()
s.to_frame(name="value")

# Single-column DataFrame → Series
df["col"].squeeze()

# DataFrame column as Series
s = df["col"]           # Copy-on-Write (default in 3.0): behaves as a copy — writing to s never mutates df
s = df["col"].copy()    # explicit independent copy

Common Constructor Patterns

# Range index (implicit, 0-based)
df = pd.DataFrame({"a": range(5)})

# DatetimeIndex as row index
dates = pd.date_range("2024-01-01", periods=5, freq="D")
df = pd.DataFrame({"v": range(5)}, index=dates)

# MultiIndex DataFrame
arrays = [["A", "A", "B", "B"], [1, 2, 1, 2]]
idx = pd.MultiIndex.from_arrays(arrays, names=["letter", "number"])
df = pd.DataFrame({"val": [10, 20, 30, 40]}, index=idx)