Pandas Day 1 — Installation, Series, DataFrames & Data Inspection

📌 Day 1 Goal Breakdown

1. Install pandas and verify version

2. Create Series from list and dictionary

3. Build DataFrame from CSV / dict with custom index

4. Explore shape, columns, dtypes attributes

5. Use head(), tail(), sample() to inspect data

1️⃣ Install pandas and verify version

How to install

Open your terminal (Command Prompt, PowerShell, or terminal in VS Code/PyCharm).

pip install pandas

If you use Jupyter or Anaconda:

conda install pandas

Verify installation & version

import pandas as pd
print(pd.__version__)

Example output:
2.0.3 (yours may be newer)

✅ pd is the standard alias for pandas — almost everyone uses it.

2️⃣ Create Series from list and dictionary

What is a Series?

A Series is like a single column of data — it has index (labels) and values.
Think of it as a Python dictionary + list combined.

From a list

import pandas as pd

# List → Series (default index 0,1,2,...)
s1 = pd.Series([10, 20, 30, 40])
print(s1)

Output:

0    10
1    20
2    30
3    40
dtype: int64

From a dictionary (keys become index)

s2 = pd.Series({'a': 100, 'b': 200, 'c': 300})
print(s2)

Output:

a    100
b    200
c    300
dtype: int64

How it works

• pandas automatically aligns data with index.

• If index is not provided, it uses 0-based integers.

• Dictionary keys become the index.

3️⃣ Build DataFrame from CSV/dict with custom index

DataFrame = multiple Series sharing the same index (like a spreadsheet)

From dictionary

data_dict = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['NYC', 'LA', 'Chicago']
}

df = pd.DataFrame(data_dict)
print(df)

Output:

      Name  Age     City
0    Alice   25      NYC
1      Bob   30       LA
2  Charlie   35  Chicago

Add custom index

df = pd.DataFrame(data_dict, index=['row1', 'row2', 'row3'])
print(df)

Output:

        Name  Age     City
row1   Alice   25      NYC
row2     Bob   30       LA
row3 Charlie   35  Chicago

From CSV file

Suppose you have data.csv:

Name,Age,City
Alice,25,NYC
Bob,30,LA
Charlie,35,Chicago

df_csv = pd.read_csv('data.csv')
print(df_csv)

⚠️ Make sure the CSV file is in the same folder as your Python script, or provide full path.

Set custom index while loading CSV

df_csv = pd.read_csv('data.csv', index_col=0)  # first column becomes index

4️⃣ Explore shape, columns, dtypes attributes

Using the DataFrame we created:

df = pd.DataFrame(data_dict)

# Number of rows and columns
print(df.shape)   # Output: (3, 3)

# Column names
print(df.columns) # Output: Index(['Name', 'Age', 'City'], dtype='object')

# Data type of each column
print(df.dtypes)

Output for dtypes:

Name     object
Age       int64
City     object
dtype: object

• object = string/text

• int64 = integer numbers

Why this matters

• shape tells you dataset size

• columns lets you access column names

• dtypes helps detect if numeric column is wrongly read as string

5️⃣ Use head(), tail(), sample() to inspect data

Let’s make a bigger DataFrame for demonstration:

import numpy as np

# Create 20 rows of dummy data
big_data = {
    'ID': range(1, 21),
    'Score': np.random.randint(50, 100, 20)
}
df_big = pd.DataFrame(big_data)

head() — first 5 rows (default)

print(df_big.head())

Output (example):

   ID  Score
0   1     78
1   2     92
2   3     65
3   4     88
4   5     73

head(10) — first 10 rows

print(df_big.head(10))

tail() — last 5 rows

print(df_big.tail())

sample() — random rows

print(df_big.sample(3))   # 3 random rows
print(df_big.sample(frac=0.1))  # 10% of rows (2 rows here)

Why these are useful

• head(): quick sanity check

• tail(): check recent/last entries

• sample(): random inspection (good for large datasets)

🧪 Practice Exercises (try these)

1. Create a Series from [5, 10, 15, 20] with custom index ['a','b','c','d'].

2. Build a DataFrame from this dictionary:
   {'Product': ['A','B'], 'Price': [100, 200], 'Stock': [10, 20]}
   Set index to ['item1', 'item2'].

3. For that DataFrame, print:

   • shape

   • column names

   • data types

4. Load any small CSV (or create one) and use .head(3), .tail(2), .sample(2).

❌ Common mistakes & how to avoid

✅ Summary of Day 1

You now know:

• Install pandas & check version

• Create Series from list/dict

• Create DataFrame from dict/CSV with custom index

• Inspect DataFrame using .shape, .columns, .dtypes

• View data with .head(), .tail(), .sample()