Series and DataFrames

Creating a Series

A Series is a labeled 1D array.

Create a Series

import pandas as pd
 
s = pd.Series([10, 20, 30], name="scores")
print(s)

Create a Series

import pandas as pd
 
s = pd.Series([10, 20, 30], name="scores")
print(s)

With a custom index

Series with explicit index

import pandas as pd
 
s = pd.Series([10, 20, 30], index=["a", "b", "c"], name="scores")
print(s)
print("Index:", s.index)

Series with explicit index

import pandas as pd
 
s = pd.Series([10, 20, 30], index=["a", "b", "c"], name="scores")
print(s)
print("Index:", s.index)

Creating a DataFrame

A DataFrame is a labeled 2D table.

From a dictionary of columns

DataFrame from dict

import pandas as pd
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Meera"],
    "age": [23, 28, 26],
    "city": ["Pune", "Delhi", "Bengaluru"],
})
 
print(df)

DataFrame from dict

import pandas as pd
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Meera"],
    "age": [23, 28, 26],
    "city": ["Pune", "Delhi", "Bengaluru"],
})
 
print(df)

From a list of dictionaries (records)

DataFrame from records

import pandas as pd
 
rows = [
    {"name": "Asha", "age": 23, "city": "Pune"},
    {"name": "Ravi", "age": 28, "city": "Delhi"},
]
 
df = pd.DataFrame(rows)
print(df)

DataFrame from records

import pandas as pd
 
rows = [
    {"name": "Asha", "age": 23, "city": "Pune"},
    {"name": "Ravi", "age": 28, "city": "Delhi"},
]
 
df = pd.DataFrame(rows)
print(df)

Understanding index and columns

df.indexdf.index labels rows
df.columnsdf.columns labels columns

Index and columns

import pandas as pd
 
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print("index:", df.index)
print("columns:", df.columns)

Index and columns

import pandas as pd
 
df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
print("index:", df.index)
print("columns:", df.columns)

Dtypes: why they matter

Pandas stores each column using a data type (dtypedtype).

Check dtypes

import pandas as pd
 
df = pd.DataFrame({
    "age": [20, 21, 22],
    "score": [90.5, 88.0, 91.0],
    "passed": [True, True, False],
})
 
print(df.dtypes)

Check dtypes

import pandas as pd
 
df = pd.DataFrame({
    "age": [20, 21, 22],
    "score": [90.5, 88.0, 91.0],
    "passed": [True, True, False],
})
 
print(df.dtypes)

Common dtype pitfalls

A numeric column can become objectobject if it contains mixed types like "N/A""N/A".
Dates start as strings unless you parse them.

We’ll fix these issues later in the phase.

🧪 Try It Yourself

Exercise 1 – Create a DataFrame

Exercise 2 – Select a Column

Exercise 3 – Filter Rows

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