Series vs DataFrame

Everything in pandas is built from two objects. Learn their shapes now and every later chapter becomes easier: a Series is like one column of a scorecard, and a DataFrame is the whole scorecard.

Definition

A Series is a one-dimensional labelled array that holds values of a single data type, together with an index (the row labels) and an optional name.

A DataFrame is a two-dimensional labelled table made of rows and columns, where each column is a Series and all columns share the same index.

• One-dimensional (Series) — just a list of values going down; it has a length but no columns.
• Two-dimensional (DataFrame) — rows and columns, like an Excel sheet or a SQL table.
• Index — the labels on the left. Both structures have one; in a DataFrame all columns share it.
• Each column is a Series — select one column from a DataFrame and you get a Series back. Select one row and you also get a Series (whose index is the column names).

Diagram

          Series                              DataFrame
   index   values                  index   player   team   runs     <- column names
  ┌───────┬──────┐                ┌──────┬────────┬──────┬──────┐
  │ Kohli │  973 │                │  0   │ Kohli  │ RCB  │  973 │
  │Buttler│  863 │                │  1   │ Buttler│ RR   │  863 │
  │ Rahul │  670 │                │  2   │ Rahul  │ PBKS │  670 │
  └───────┴──────┘                └──────┴────────┴──────┴──────┘
   name: "runs"                              │        │      │
   dtype: int64                           Series   Series  Series
                                        (each column is a Series,
                                         all sharing the same index)

Syntax

pd.Series(data=None, index=None, dtype=None, name=None)
pd.DataFrame(data=None, index=None, columns=None, dtype=None)

Both constructors get a full chapter of their own — this topic is just the overview.

Example — one of each

import pandas as pd

runs = pd.Series([973, 863, 670], index=["Kohli", "Buttler", "Rahul"], name="runs")
print(runs)
print(type(runs))

Kohli      973
Buttler    863
Rahul      670
Name: runs, dtype: int64
<class 'pandas.Series'>

A printed Series shows index on the left, values on the right, then a footer with the name and dtype.

df = pd.DataFrame({
    "player": ["Virat Kohli", "Jos Buttler", "KL Rahul", "Shubman Gill"],
    "team": ["RCB", "RR", "PBKS", "GT"],
    "season": [2016, 2022, 2020, 2023],
    "runs": [973, 863, 670, 890],
    "average": [81.08, 57.53, 55.83, 59.33],
})
print(type(df))
print(type(df["runs"]))
print(df["runs"])

<class 'pandas.DataFrame'>
<class 'pandas.Series'>
0    973
1    863
2    670
3    890
Name: runs, dtype: int64

How it works: df["runs"] pulls one column out of the DataFrame. It comes back as a Series whose name is the column name (runs) and whose index is the DataFrame's index (0–3).

Example — a row is also a Series

print(df.loc[0])

player     Virat Kohli
team               RCB
season            2016
runs               973
average          81.08
Name: 0, dtype: object

How it works: .loc[0] selects the row whose label is 0. The result is a Series where the index is the column names and the name is the row label (0). Because a row mixes text and numbers, its dtype has to be the general object.

Comparison

Common mistakes

• ❌ pd.series(...) or pd.dataframe(...). ✅ Class names are capitalised: pd.Series, pd.DataFrame (capital F too).
• ❌ Expecting df["runs"] to return a DataFrame. ✅ Single brackets → Series; double brackets df[["runs"]] → DataFrame.
• ❌ Thinking a Series has columns. ✅ A Series has only an index and values; series.columns raises AttributeError.
• ❌ Using .dtype on a DataFrame. ✅ A DataFrame has .dtypes (plural) — one per column.

Interview tip

Q: What is the relationship between a Series and a DataFrame? A: A DataFrame is a collection of Series that share the same index — one Series per column. Selecting a single column (df["col"]) or a single row (df.loc[label]) returns a Series.