Pandas · Series and DataFrame

• A Series is a 1-D NumPy array plus an index (labels).
• A DataFrame is a table: a dict of Series (columns) that share one row index.

The important new idea is alignment. When you add two Series, pandas matches them by index label, not by position. A label present in only one of them gives NaN.

Useful first commands: df.head(), df.info(), df.describe(), df.shape, df.dtypes.

import pandas as pd

s = pd.Series([88, 79, 95], index=["maths", "physics", "python"], name="marks")
print(s)
print(s["python"], s.mean().round(2))

bonus = pd.Series({"python": 5, "maths": 2, "chemistry": 10})
print(s + bonus)                       # aligned by label

df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Sahaj", "Meet"],
    "branch": ["CSE", "ICT", "ICT", "ME"],
    "cgpa": [8.4, 7.1, 8.9, 6.8],
})
print(df)
print(df.shape, list(df.columns))
print(df.describe().round(2))

Output:

maths      88
physics    79
python     95
Name: marks, dtype: int64
95 87.33
chemistry      NaN
maths         90.0
physics        NaN
python       100.0
dtype: float64
    name branch  cgpa
0   Asha    CSE   8.4
1   Ravi    ICT   7.1
2  Sahaj    ICT   8.9
3   Meet     ME   6.8
(4, 3) ['name', 'branch', 'cgpa']
       cgpa
count  4.00
mean   7.80
std    1.01
min    6.80
25%    7.02
50%    7.75
75%    8.52
max    8.90