Pandas Basics for Data Science

Pandas is one of the most powerful Python libraries for data cleaning, manipulation, analysis, and exploration. It provides two main data structures:

• Series → 1-dimensional labeled data

• DataFrame → 2-dimensional tabular data (rows & columns)

1. Importing Pandas

import pandas as pd

2. Creating Data Structures

(A) Creating a Series

s = pd.Series([10, 20, 30, 40])
print(s)

(B) Creating a DataFrame

data = {
    'Name': ['Ram', 'Shyam', 'Mohan'],
    'Age': [25, 30, 35],
    'City': ['Patna', 'Delhi', 'Mumbai']
}
df = pd.DataFrame(data)
print(df)

3. Reading & Writing Data

Read CSV

df = pd.read_csv("data.csv")

Write CSV

df.to_csv("data_output.csv", index=False)

Read Excel

df = pd.read_excel("data.xlsx")

Write Excel

df.to_excel("output.xlsx", index=False)

4. Viewing Data

df.head()       # first 5 rows
df.tail()       # last 5 rows
df.shape        # rows, columns
df.info()       # data types
df.describe()   # summary stats

5. Selecting Columns & Rows

Select single column

df['Age']

Select multiple columns

df[['Name', 'City']]

Select rows by index

df.loc[0]      # by label
df.iloc[0]     # by position

Select row range

df[5:10]

6. Filtering Data

df[df['Age'] > 30]
df[(df['Age'] > 25) & (df['City'] == 'Delhi')]

7. Adding / Updating Columns

df['Salary'] = [50000, 60000, 70000]
df['Age_in_5yrs'] = df['Age'] + 5

8. Deleting Columns or Rows

Delete column

df.drop('Salary', axis=1, inplace=True)

Delete rows

df.drop(0, axis=0, inplace=True)

9. Handling Missing Values

Check missing

df.isnull().sum()

Fill missing

df.fillna(0, inplace=True)

Drop missing

df.dropna(inplace=True)

10. Sorting Data

df.sort_values('Age')
df.sort_values('Age', ascending=False)

11. Grouping & Aggregation

df.groupby('City')['Age'].mean()
df.groupby('City').agg({'Age': 'mean', 'Salary': 'sum'})

12. Merging & Joining DataFrames

Merge

pd.merge(df1, df2, on='id')

Concatenate

pd.concat([df1, df2])

13. Pandas with NumPy Operations

df['Double_Age'] = df['Age'] * 2

Summary Table