Pandas

Python · Reference cheat sheet

📋 Overview

Pandas is the go-to tabular data library. Install with pip install pandas. Core types: Series (1D labeled) and DataFrame (2D table). Typical flow: read_csv → filter/transform → groupby / merge → to_csv.

🔧 Core concepts

API	Role
`pd.Series(data)`	1D labeled array
`pd.DataFrame(data)`	Table (columns + index)
`pd.read_csv(path)`	Load CSV
`df[mask]` / `.loc` / `.iloc`	Filter / select
`df.groupby(col)`	Split-apply-combine
`pd.merge(left, right, on=...)`	Join tables
`df.to_csv(path, index=False)`	Export
`df.head()` / `.info()` / `.describe()`	Inspect
`df.assign`	Add columns functionally
`df.sort_values`	Order rows
`df.pivot_table`	Reshape aggregates
`pd.concat`	Stack frames

Theory: A DataFrame is a labeled column store — vectorized ops beat Python loops. Indexing aligns on labels during arithmetic. Missing data is NaN/NaT/<NA>; most reductions skip NaNs by default (skipna=True). Prefer explicit .loc writes over chained [][].

💡 Examples

Read and filter:

import pandas as pd

df = pd.read_csv("sales.csv")
active = df[df["status"] == "active"]
big = df.loc[df["amount"] > 100, ["name", "amount"]]
print(df.head(), active.shape)

Groupby:

import pandas as pd

df = pd.read_csv("orders.csv")
summary = (
    df.groupby("region", as_index=False)["amount"]
    .sum()
    .sort_values("amount", ascending=False)
)
print(summary)

Merge:

import pandas as pd

orders = pd.read_csv("orders.csv")
customers = pd.read_csv("customers.csv")
joined = pd.merge(orders, customers, on="customer_id", how="left")
print(joined.head())

Write CSV:

import pandas as pd

df = pd.DataFrame({"a": [1, 2], "b": ["x", "y"]})
df.to_csv("out.csv", index=False, encoding="utf-8")

Assign + sort:

import pandas as pd

df = pd.DataFrame({"n": [3, 1, 2], "x": [10, 20, 30]})
df = (
    df.assign(double=lambda d: d["x"] * 2)
    .sort_values("n")
    .reset_index(drop=True)
)
print(df)

Pivot table:

import pandas as pd

sales = pd.DataFrame({
    "region": ["E", "E", "W", "W"],
    "product": ["a", "b", "a", "b"],
    "amount": [10, 4, 8, 6],
})
print(sales.pivot_table(index="region", columns="product", values="amount", aggfunc="sum"))

Value counts:

print(df["status"].value_counts(dropna=False))
print(df["status"].value_counts(normalize=True))

⚠️ Pitfalls

Install first: pip install pandas (not in the stdlib).
Chained indexing (df[...][...] =) can fail silently — prefer .loc.
groupby default may leave the key as index; use as_index=False when you want a flat frame.
Merges duplicate key columns as _x / _y if names collide — set suffixes.
Large CSVs: pass usecols, dtype, or chunk with chunksize=.

Pandas

Pandas

📋 Overview

🔧 Core concepts

💡 Examples

⚠️ Pitfalls

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