pandas-3-0-3

name

pandas-3-0-3

description

Complete toolkit for pandas 3.0.3 providing DataFrame and Series data structures, tabular data manipulation, groupby aggregations, merging/joining, reshaping, time series analysis, I/O for CSV/JSON/Parquet/Excel/SQL/Iceberg, PyArrow integration, Copy-on-Write semantics, and the new default string dtype. Use when building Python programs that require data wrangling, exploratory analysis, ETL pipelines, statistical summaries, or any workflow centered on labeled tabular data.

pandas 3.0.3

Overview

pandas is the standard Python library for data manipulation and analysis. It provides two core data structures — Series (1D labeled array) and DataFrame (2D labeled table) — along with tools for reading/writing data, filtering, grouping, reshaping, merging, time series operations, and statistical summaries.

pandas 3.0 introduces three major changes:

1. Dedicated string dtype by default — String columns are now inferred as str (backed by PyArrow when available), replacing the legacy object dtype.
2. Copy-on-Write (CoW) — All indexing and method results behave as copies. Chained assignment no longer works, and SettingWithCopyWarning is removed.
3. pd.col() expressions — Column references without lambda functions in assign(), loc[], and similar methods.

When to Use

• Reading, cleaning, transforming, or writing tabular data (CSV, JSON, Parquet, Excel, SQL)
• Grouping data and computing aggregations (split-apply-combine)
• Merging or joining multiple tables on keys
• Reshaping data with pivot tables, melt, stack/unstack
• Time series analysis with resampling, rolling windows, or timezone handling
• Vectorized string operations on text columns
• Any Python data pipeline that operates on labeled rows and columns

Core Concepts

Data Structures

• DataFrame — 2D labeled table with named columns and an index. The primary workhorse.
• Series — 1D labeled array. A single column from a DataFrame is a Series.
• Index — Immutable axis labels. Supports RangeIndex, DatetimeIndex, MultiIndex, etc.

Key dtypes (pandas 3.0)

dtype	description
str	Default string type (PyArrow-backed if available). Missing value is NaN.
int64, float64, bool	Standard NumPy types.
Int64, boolean	Nullable variants using pd.NA.
datetime64[ns]	Timestamps. Use DatetimeIndex for time-indexed data.
timedelta64[ns]	Time durations.
category	Fixed-set categorical data. Memory-efficient for repeated values.

Copy-on-Write (CoW)

Every indexing operation or method returning a new DataFrame/Series behaves as if it were a copy. Modifying a subset never modifies the original. Direct assignment to the object itself is the only way to mutate it:

df["new_col"] = df["a"] + df["b"]  # OK — modifies df directly
subset = df[df["a"] > 0]
subset["b"] = 99  # Does NOT modify df (CoW semantics)

pd.col() Expressions

Reference columns without lambda wrappers:

df.assign(total=pd.col("price") * pd.col("qty"))
df.loc[pd.col("status") == "active", "flag"] = 1

Usage Examples

Create and inspect a DataFrame

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "score": [88.5, 92.0, 78.3],
})
print(df.dtypes)
# name     str
# age      int64
# score   float64

Read CSV, filter, group, and write Parquet

df = pd.read_csv("data.csv")
filtered = df.loc[df["revenue"] > 0]
summary = filtered.groupby("region").agg(
    total=("revenue", "sum"),
    avg=("score", "mean"),
    count=("id", "count"),
)
summary.to_parquet("output.parquet")

Merge two DataFrames

merged = pd.merge(orders, customers, on="customer_id", how="left")

Reshape with pivot table

pivot = df.pivot_table(values="sales", index="month", columns="category", aggfunc="sum")

Time series resampling

ts = pd.read_csv("prices.csv", parse_dates=["date"], index_col="date")
daily = ts["price"].resample("D").mean()

Advanced Topics

Data Structures: Series, DataFrame construction, attributes, dtypes, pandas 3.0 str default → Data Structures

Indexing and Selection: .loc/.iloc, boolean indexing, query(), slicing, isin() → Indexing and Selection

Data Manipulation: groupby, merge/join/concat, pivot/melt/stack, pd.col() expressions → Data Manipulation

I/O Operations: CSV, JSON, Parquet, Excel, SQL, Iceberg, ORC, Arrow PyCapsule Interface → I/O Operations

Time Series: Timestamps, DatetimeIndex, resampling, timezones, DateOffset, date_range() → Time Series

Text and Strings: .str accessor, StringDtype variants, pandas 3.0 string migration → Text and Strings

Advanced Topics: Copy-on-Write, PyArrow backend, windowing, visualization/Styler, performance, categorical data, missing data, UDFs → Advanced Topics