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Pandas Introduction

Pandas is the main library for tabular data in Python. Series is one column. DataFrame is a table. It sits on top of NumPy.

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What Is Pandas?

Use Pandas to load CSV/JSON, clean missing values, filter rows, group, merge, and plot. It is the usual first stop after NumPy in a data-science workflow.

Chapters in This Section

ChapterYou will learn
Getting StartedInstall and import
SeriesOne-dimensional labeled array
DataFramesTables with rows and columns
Read CSVread_csv
Read JSONread_json
Analyzinghead, info, describe
Selectingloc and iloc
CleaningMissing values and duplicates
GroupBySplit-apply-combine
MergeJoin DataFrames
PlottingQuick charts from a DataFrame

Series and DataFrame

A Series is a labelled 1-D column; a DataFrame is a 2-D table of Series sharing an index — think of a spreadsheet in Python.

import pandas as pd

df = pd.DataFrame({
    "name": ["Ann", "Bob", "Cara"],
    "age":  [30, 25, 35],
    "city": ["NYC", "LA", "NYC"],
})

print(df.head())        # first rows
print(df["age"].mean()) # 30.0
print(df.shape)         # (3, 3)

Filtering, Selecting, Grouping

# boolean filtering
print(df[df["age"] > 28])

# select by label / position
print(df.loc[0, "name"])   # Ann
print(df.iloc[0, 0])       # Ann

# group and aggregate
print(df.groupby("city")["age"].mean())
# city
# LA     25.0
# NYC    32.5

Use .loc[] for label-based access and .iloc[] for position-based access — mixing them up is a common beginner error.

Try It Yourself

Exercise 1: From the DataFrame above, print the names of everyone in NYC.

Show solution
print(df[df["city"] == "NYC"]["name"].tolist())   # ['Ann', 'Cara']

Exercise 2: Add a column senior that is True when age > 30.

Show solution
df["senior"] = df["age"] > 30
print(df)

Key Takeaways

  • DataFrames are labelled 2-D tables; Series are single columns.
  • Filter with boolean masks; select with .loc/.iloc.
  • groupby + an aggregation summarizes data fast.
  • Read/write CSV, Excel, JSON, and SQL with one call each.

📘 Real-World Deep Dive

pandas wraps NumPy arrays with named columns and date indexes, and adds groups, joins, missing-data handling, and rich I/O. Once you think in DataFrames, almost any tabular task becomes legible.

Real-Life Scenario

Realistic sales pipeline: load a 100 k-row CSV, normalise dates, audit per-customer revenue in a weekly time window, and emit the result back as Parquet.

Real-Life Example

import pandas as pd

# In real life: df = pd.read_csv("orders.csv", parse_dates=["created_at"])
df = pd.DataFrame({
    "customer":   ["ada", "bo",  "cy", "de"] * 250,
    "created_at": pd.date_range("2026-01-01", periods=1000, freq="h"),
    "amount":     [10, 20, 5, 30] * 250,
})

df = df.assign(amount=df["amount"].astype("float32"))

# Resample per customer × week
grouped = (
    df.set_index("created_at")
      .groupby("customer")
      .resample("W")
      .agg(revenue=("amount", "sum"), orders=("amount", "count"))
      .reset_index()
)
print(grouped.head())
print("
week with peak revenue per customer:")
peak = grouped.loc[grouped.groupby("customer")["revenue"].idxmax()]
print(peak)

# Out as Parquet — small, typed, columnar
# grouped.to_parquet("weekly.parquet")

Expected Output

    customer created_at    revenue  orders
0         ada 2026-01-05       60.0       2
1         ada 2026-01-12       60.0       2
...
week with peak revenue per customer:
customer     created_at  revenue  orders
ada         2026-04-27     720.0     12
bo          2026-05-04     600.0     10
...

Common mistakes

  • df[df.col > x] returns a copy and triggers a SettingWithCopyWarning on writes; assign via df.loc[mask, col].
  • Pandas treats a regular list-of-dicts as object dtype; cast with pd.to_numeric / astype("category") for big speedups.
  • df.iterrows() is O(n) and slow; use itertuples() or df.eval / df.query for batched work.

🚀 Performance & Best Practices

  • Switch the IO engine: pd.read_csv(..., engine="pyarrow", dtype_backend="pyarrow") is much faster.
  • .astype("category") collapses repeated text into a small dictionary — sort and group-by speed up dramatically.
  • For datasets > 1 GB, work with modin / polars instead of eager pandas.

🧪 Try It Yourself

  1. Add a per-customer moving-7-day revenue column.
  2. Compute week-over-week growth and flag negative weeks.
  3. Read the source as Parquet + pyarrow and compare with CSV performance.

FAQ: Pandas Introduction

Common questions about this page.

What is Pandas Introduction?

Pandas Introduction is a Pandas lesson that explains pandas introduction in Pandas. Pandas is the main library for tabular data in Python. Series is one column. DataFrame is a table. It sits on top of NumPy. It is written for beginners who want a clear definition and working examples.

Should I run pandas introduction examples locally for better learning?

Yes. Use the browser editor on StudyGrid for a quick check, then Download the example and run it on your computer. Local runs show real errors and the real toolchain, which is one of the fastest ways to learn pandas introduction in this Pandas Pandas lesson (Pandas Introduction).

How do I use pandas introduction in Pandas?

To use pandas introduction in Pandas, follow the examples on this StudyGrid page. Copy a snippet, run it in the browser, then Download and run it locally for better learning. Change the values and compare the output.

What is the syntax of pandas introduction?

This Pandas Introduction tutorial shows pandas introduction syntax with short Pandas examples. Use the code blocks in this lesson for the exact statements, then try them in your editor.

Pandas Introduction example for beginners

Yes. This page includes a beginner pandas introduction example you can copy and run. It is designed for searches such as "pandas introduction for beginners", "pandas introduction example", and "how to use pandas introduction".

What are common mistakes with pandas introduction?

Common pandas introduction mistakes include wrong syntax, mixing types, and skipping practice. Work through this Pandas chapter in order, run every example, and check the output before moving on.

Why should I learn pandas introduction?

Pandas Introduction is used in real Pandas work. Learning pandas introduction helps you write clearer programs and continue the Pandas tutorial on StudyGrid.

Is Pandas Introduction free to learn online?

Yes. You can learn pandas introduction free on StudyGrid (studygrid.in). This chapter is part of the Pandas path and includes examples, syntax, and next-step links.