Pandas is one of the most powerful and easy-to-use Python libraries for data analysis.

In this post, we’ll walk through how pandas works - from creating Series & DataFrame to analyzing data

Pandas is a popular Python library used for data analysis and manipulation. It provides two main data structures:

1. Series → One-dimensional labeled array (like a column in Excel)

2. DataFrame → Two-dimensional labeled data (like an Excel sheet)

🧩 Step 1: Creating a Series

A Series in pandas is like a one-dimensional array (similar to a list), but with labels (index) attached to each value.

import pandas as pd

series1 = pd.Series(data = [10, 20, 30, 40, 50], index=['a', 'b', 'c', 'd', 'e'])
print(series1)

Output:

a    10
b    20
c    30
d    40
e    50
dtype: int64

Explanation:

• The values are [10, 20, 30, 40, 50]

• The index labels are ['a', 'b', 'c', 'd', 'e']

• First list by default considered as data list, its optional to write data

If you remove the index argument, pandas automatically assigns default index numbers starting from 0.

series1 = pd.Series([10, 20, 30, 40, 50])

Output:

0    10
1    20
2    30
3    40
4    50

💡 Tip: If you remove only the keyword index= but still pass the index list, it will still work fine because pandas detects the position of arguments automatically

🚗 Step 2: Creating Series from a Dictionary

You can also create a Series directly from a Python dictionary.

entries = { 
  'swift': 649000,
  'santro': 391000,
  'audi': 5476000,
  'elantra': 1786000,
  'bolero': 1091000
}

car_series1 = pd.Series(data=entries)

Alternatively, you can use separate lists for values and index:

prices = [649000, 391000, 5476000, 1786000, 1091000]
carnames = ['swift', 'santro', 'audi', 'elantra', 'bolero']

car_series2 = pd.Series(data=prices, index=carnames)

✅ Both car_series1 and car_series2 will work the same and produce identical output because the data and indices match.

🌟Aap check bhi kar sakte ho: print(car_series1.equals(car_series2))

🎯 Step 3: Accessing Data – .loc[] vs .iloc[]

To access elements in a Series, pandas provides two powerful methods:

.loc[] → Label-based indexing

Use the index name.

car_series1.loc['audi']

Output:

5476000

.iloc[] → Integer position-based indexing

Use the index position (number).

car_series1.iloc[2]

Output:

5476000

🪄 In short:

⚖️ Step 4: What’s the difference between car_series.loc['audi'] and car_series['audi']?

Both work the same for simple Series lookups — they return the value associated with the label 'audi'.

However:

• .loc[] is more explicit and is the preferred method when writing clear, maintainable code.

• Direct indexing ([]) is a shortcut that works fine in most cases.

🗣️ Chhoti baat, par important hai! → Use .loc[] when working on professional projects to avoid confusion later.

🔍 Step 5: Boolean Filtering

Boolean filtering allows you to select data based on conditions.

Example:

print(car_series1[car_series1 > 1000000])

Output:

swift      649000
audi      5476000
elantra   1786000
bolero    1091000
dtype: int64

You can also combine multiple conditions:

car_series1[(car_series1 > 1000000) & (car_series1 < 5000000)]

📂 Step 6: Reading CSV Files in Pandas

There are multiple ways to load a CSV file:

df = pd.read_csv("data.csv")               # Most common
df = pd.read_table("data.csv", sep=',')    # Alternate
df = pd.read_csv("data.csv", header=None)  # Without headers
df = pd.read_csv("data.csv", nrows=10)     # Read first 10 rows

💡 You can even load files from URLs:

df = pd.read_csv("https://example.com/data.csv")

🧾 Step 7: DataFrame Exploration Methods

Once your data is loaded, you can quickly inspect it using these handy tools:

🔹 .info()

Shows a summary of the DataFrame — structure, data types, and non-null counts.

df.info()

🔹 .describe()

Gives statistical summaries of numeric columns.

df.describe()

To include all columns (even strings):

df.describe(include='all')

🧮 Step 8: .shape and .columns

These attributes help you understand the structure of your dataset.

df.shape     # Returns (rows, columns)
df.columns   # Returns list of column names

Example:

print("Rows:", df.shape[0])
print("Columns:", df.shape[1])

Output:

Rows: 4
Columns: 3

Here:

• df.shape[0] → gives number of rows

• df.shape[1] → gives number of columns

📌 Yaad rakho: .shape and .columns are attributes, not methods — no parentheses () needed.

🏁 Final Thoughts

By now, we’ve covered:

• Creating and accessing Series

• Using .loc[] and .iloc[]

• Boolean filtering

• Reading CSV files

• Exploring DataFrames with .info(), .describe(), .shape, and .columns

💬 Ek line mein kahun toh — Pandas is not just a library; it’s a superpower for anyone who works with data.

Keep experimenting and practicing with real datasets — that’s how you’ll truly master it.

✍️ Written by: Krunal R. Mandlekar

📧 krunalmandlekar@gmail.com
🔗 www.linkedin.com/in/krunal-mandlekar-397b11313