Pandas: Working with Data Structures

Creating Series and DataFrames#

Creating a Pandas Series#

A Pandas Series is basically a glorified one-dimensional array, except it comes with labels and makes you feel smarter for using it.

• From a List:

import pandas as pd
s = pd.Series([42, 23, 16, 15, 8, 4])
print(s)

• From a Dictionary:

data = {"Apples": 3, "Bananas": 5, "Cherries": 7}
s = pd.Series(data)
print(s)

• From a NumPy Array:

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
s = pd.Series(arr)
print(s)

Creating a Pandas DataFrame#

A DataFrame is like an overachieving spreadsheet on steroids. Here’s how you create one:

• From a Dictionary:

data = {"Name": ["Alice", "Bob", "Charlie"], "Age": [25, 30, 35]}
df = pd.DataFrame(data)
print(df)

• From a List of Lists:

data = [["Alice", 25], ["Bob", 30], ["Charlie", 35]]
df = pd.DataFrame(data, columns=["Name", "Age"])
print(df)

• From a NumPy Array:

arr = np.array([[1, 2], [3, 4], [5, 6]])
df = pd.DataFrame(arr, columns=["Column1", "Column2"])
print(df)

Importing Data from Various Sources#

Let’s be real: no one enjoys manually typing in data. Here’s how to import it like a sane person:

• Reading CSV Files:

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

• Reading Excel Files:

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

• Reading JSON Files:

df = pd.read_json("data.json")

• Reading from a Database:

import sqlite3
conn = sqlite3.connect("database.db")
df = pd.read_sql("SELECT * FROM table_name", conn)

Viewing, Summarizing, and Exploring Datasets#

Your dataset is a mysterious black box until you poke at it. Here’s how:

• First and Last Few Rows:

print(df.head())  # First 5 rows
print(df.tail())  # Last 5 rows

• Checking Dataset Structure:

print(df.info())

• Summarizing Numerical Data:

print(df.describe())

• Checking for Missing Values:

print(df.isnull().sum())

• Getting Unique Values and Value Counts:

print(df["column_name"].unique())
print(df["column_name"].value_counts())

Selecting, Indexing, and Slicing Data#

Time to chop up your dataset like a chef:

• Selecting Columns:

print(df["column_name"])

• Selecting Rows by Label:

print(df.loc[0])  # First row

• Selecting Rows by Index Position:

print(df.iloc[0])  # First row

• Filtering Rows with Conditions:

filtered_df = df[df["Age"] > 30]
print(filtered_df)

• Resetting and Setting Index:

df.set_index("column_name", inplace=True)
df.reset_index(inplace=True)

Hands-On Exercise#

1. Create Series and DataFrames: Generate a Pandas Series and DataFrame using lists, dictionaries, and NumPy arrays.
2. Load External Data: Import a dataset from a CSV, Excel, and JSON file.
3. Explore a Dataset: Load a sample dataset and apply .head(), .info(), and .describe().
4. Select and Filter Data: Use .loc[] and .iloc[] to extract specific rows and columns.

References#

• Pandas Data Structures