Python Core Libraries

Overview

Teaching: 130 min
Exercises: 130 min

Questions

What are the basics of Numpy?

What are the basics of Pandas?

What are the basics of Matplotlib?

Objectives

Learn the basics of Numpy.

Learn the basics of Pandas

Learn the basics of Matplotlib.

Python Core Libraries

NumPy

Agenda

Learn the basics of NumPy, a library for numerical computing in Python.
Understand and create arrays, the core data structure in NumPy.
Perform basic mathematical operations on arrays.
Learn advanced operations like reshaping, slicing, and broadcasting.

Introduction to NumPy

NumPy stands for “Numerical Python” and provides fast, efficient, and flexible array operations.
The core object in NumPy is the ndarray (N-dimensional array).
Designed for scientific computation: useful linear algebra, Fourier transform, and random number capabilities etc.

Import the NumPy library

import numpy as np

# Check the version of NumPy
print(np.__version__)

Creating NumPy Arrays

Creating Arrays from Lists

Create a 1D array

arr = np.array([1, 2, 3, 4])
print(arr)

Create a 2D array

arr_2d = np.array([[1, 2], [3, 4]])
print(arr_2d)

Create a 3D Arrays

c = np.array([[[1,1,2], [2,3,3]], [[1,1,1], [1,1,1]], [[1,1,1], [1,1,1]]])

Creating Arrays with NumPy Functions

Create an array of zeros

zeros = np.zeros((2, 3))  # 2 rows, 3 columns
print(zeros)

Create an array of ones

ones = np.ones((3, 2))  # 3 rows, 2 columns
print(ones)

Random numbers

a = np.random.rand(4)       # uniform in [0, 1]

b = np.random.randn(4)      # Gaussian

Create an array with a range of numbers

range_arr = np.arange(0, 10, 2)  # Start at 0, stop before 10, step by 2
print(range_arr)

Create an array with evenly spaced numbers

linspace_arr = np.linspace(0, 1, 5)  # Start at 0, stop at 1, 5 evenly spaced values
print(linspace_arr)

Basic Array Operations

Mathematical Operations

Perform basic math operations

arr = np.array([1, 2, 3, 4])
print(arr + 2)  # Add 2 to each element
print(arr * 3)  # Multiply each element by 3

Perform element-wise operations with another array

arr2 = np.array([5, 6, 7, 8])
print(arr + arr2)  # Element-wise addition
print(arr * arr2)  # Element-wise multiplication

Array Properties

Get properties of an array

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)  # Shape of the array (rows, columns)
print(arr.size)   # Total number of elements
print(arr.dtype)  # Data type of elements

Indexing and Slicing

Indexing

Access elements of a 1D array

arr = np.array([10, 20, 30, 40])
print(arr[0])  # First element
print(arr[-1])  # Last element

Access elements of a 2D array

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(arr_2d[0, 1])  # Element in first row, second column
print(arr_2d[1, 2])  # Element in second row, third column

Slicing

Slice a 1D array

arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])  # Elements from index 1 to 3
print(arr[:3])   # First three elements

Slice a 2D array

arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr_2d[:2, 1:])  # First two rows, columns 2 and 3
print(arr_2d[1:, :2])  # Last two rows, first two columns

Reshaping Arrays

Changing the Shape of an Array

Reshape a 1D array to a 2D array

arr = np.arange(1, 10)
reshaped = arr.reshape(3, 3)  # 3 rows, 3 columns
print(reshaped)

Flatten a 2D array back to 1D

flattened = reshaped.flatten()
print(flattened)

Broadcasting

Broadcasting allows operations between arrays of different shapes.

Add a scalar to a 2D array

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(arr_2d + 10)

Add a 1D array to a 2D array

row = np.array([1, 2, 3])
print(arr_2d + row)

Asking For Help

np.add?
np.array?
np.arr*?
np.con*?

Key Points:

NumPy arrays are efficient for numerical computations.
Perform operations like addition, multiplication, and broadcasting directly on arrays.
Use slicing and indexing to access or modify specific elements.

Pandas

Agenda

Learn the basics of the Pandas library.
Understand how to work with Series and DataFrames.
Perform data manipulation tasks like filtering, sorting, and aggregating.
Learn how to handle missing data.

Introduction to Pandas

Pandas is a powerful library for data manipulation and analysis.
The two main data structures are:
- Series: A one-dimensional labeled array.
- DataFrame: A two-dimensional table-like data structure.

Import the Pandas library

import pandas as pd

# Check the version of Pandas
print(pd.__version__)

Pandas Series

A Series is a one-dimensional array with labels (index).
It is similar to a column in a spreadsheet.

# Create a Series from a list
data = [10, 20, 30, 40]
series = pd.Series(data, index=["A", "B", "C", "D"])
print(series)

# Accessing elements in a Series
print(series["B"])  # Output: 20

Pandas DataFrame

A DataFrame is a two-dimensional structure with rows and columns.
Think of it as a table in a database or an Excel sheet.

Create a DataFrame from a dictionary

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

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

Viewing DataFrame

Use DataFrame.head() and DataFrame.tail() to view the top and bottom rows of the frame respectively

Print the head of the DataFrame

df.head()

Print the tail of the DataFrame

df.tail()

Display DataFrame information

df.info()

Display the data types

df.dtypes

Display statistical summary of DataFrame

df.describe()

Display the DataFrame index and columns

Use DataFrame.index or DataFrame.columns to get the DataFrame index and columns

Get the DataFrame index

df.index

Get the DataFrame columns

df.columns

Accessing a DataFrame

# Accessing a column
print(df["Name"])

# Accessing rows using loc
print(df.loc[1])  

# Accessing rows using iloc
print(df.iloc[2]) 

Adding New Columns

df['Experience'] = np.random.randint(1, 5, size=len(df))

df.head()

Dropping Columns

Use the drop() method to remove columns.
Specify axis=1 to indicate columns.
Use inplace=True to modify the DataFrame directly (optional).

# Dropping a Single Column
df_dropped = df.drop("City", axis=1)
print(df_dropped)

# Alternatively, drop in place (modifies the original DataFrame)
df.drop("City", axis=1, inplace=True)
print(df)

# Dropping Multiple Columns
df = pd.DataFrame(data)  # Recreate the original DataFrame
df_dropped = df.drop(["Age", "City"], axis=1)
print(df_dropped)

Dropping Rows

Use the drop() method to remove rows.
Specify axis=0 to indicate rows (default behavior).
Use inplace=True to modify the DataFrame directly (optional).

# Drop the row with index 1
df_dropped = df.drop(1, axis=0)
print(df_dropped)

# Alternatively, drop in place
df.drop(1, axis=0, inplace=True)
print(df)

# Drop multiple rows
df = pd.DataFrame(data)  # Recreate the original DataFrame
df_dropped = df.drop([0, 2], axis=0)
print(df_dropped)

Reading and Writing Data

Pandas supports reading and writing data in multiple formats (CSV, Excel, JSON, etc.).

GDP data

Read data from a CSV file

df = pd.read_csv("example.csv")
print(df)

Write data to a CSV file

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

Reading Excel Files

# Read data from an Excel file
df = pd.read_excel("example.xlsx", sheet_name="Sheet1")  # Specify the sheet name

# Read the first 5 rows (head) of the DataFrame
print(df.head())

Writing Excel Files

To save a DataFrame to an Excel file, use the to_excel() function.

# Save DataFrame to an Excel file
df.to_excel("output.xlsx", index=False)  # Save without including the index

Writing Multiple Sheets

To write multiple DataFrames to different sheets in the same Excel file, use ExcelWriter.

# Create multiple DataFrames
df1 = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
df2 = pd.DataFrame({"X": [7, 8, 9], "Y": [10, 11, 12]})

# Write to multiple sheets
with pd.ExcelWriter("multi_sheet_output.xlsx") as writer:
    df1.to_excel(writer, sheet_name="Sheet1", index=False)
    df2.to_excel(writer, sheet_name="Sheet2", index=False)

Reading JSON Files

# Read data from a JSON file
df = pd.read_json("example.json")

# Display the first 5 rows
print(df.head())

Writing JSON Files

To save a DataFrame to a JSON file, use the to_json() function.

# Save DataFrame in a column-oriented JSON format
df.to_json("column_oriented.json", orient="columns")

Data Manipulation

Filtering: Select specific rows or columns.
Sorting: Sort data based on column values.
Aggregating: Perform operations like sum, mean, count, etc.

Filtering

# Filter rows where Age > 25
filtered_df = df[df["Age"] > 25]
print(filtered_df)

# Filter rows where Age > 25 and City is "Chicago"
filtered_df = df[(df["Age"] > 25) & (df["City"] == "Chicago")]
print(filtered_df)

Sort by Age in descending order

sorted_df = df.sort_values("Age", ascending=False)
print(sorted_df)

Aggregations

# Calculate the mean age
mean_age = df["Age"].mean()
print(f"Mean Age: {mean_age}")

Selecting Columns and Rows in Pandas

Selecting Columns

You can select one or multiple columns from a DataFrame.
Columns can be accessed using square brackets [] or dot notation (only for single-column names without spaces).

Selecting a Single Column

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}
df = pd.DataFrame(data)

# Select a single column
age_column = df["Age"]
print(age_column)

# Alternatively, use dot notation
city_column = df.City
print(city_column)

Selecting Multiple Columns

subset = df[["Name", "City"]]
print(subset)

Renaming Columns

df.rename(columns={"Age": "Years"}, inplace=True)
print(df)

Selecting Rows

Use .loc[] for label-based selection (index labels).
Use .iloc[] for position-based selection (index positions).

Selecting Rows by Index Label (.loc[])

# Select a single row by index label
row_1 = df.loc[1]
print(row_1)

# Select multiple rows by index labels
rows = df.loc[[0, 2]]
print(rows)

Selecting Rows by Index Position (.iloc[])

# Select a single row by index position
row_0 = df.iloc[0]
print(row_0)

# Select multiple rows by index positions
rows = df.iloc[[0, 2]]
print(rows)

Selecting Specific Rows and Columns

Combine .loc[] or .iloc[] to select specific rows and columns.

Using .loc[] for Specific Rows and Columns

# Select rows 0 and 2, and columns "Name" and "City"
subset = df.loc[[0, 2], ["Name", "City"]]
print(subset)

Using .iloc[] for Specific Rows and Columns

# Select rows 0 and 2, and columns at positions 0 and 2
subset = df.iloc[[0, 2], [0, 2]]
print(subset)

Handling Missing Data

Use isna() to identify missing values.
Fill missing values with fillna().
Remove rows/columns with missing values using dropna().

Handling Missing Data

Create a DataFrame with missing values

data = {
    "Name": ["Alice", "Bob", None],
    "Age": [25, None, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}
df = pd.DataFrame(data)

Check for missing values

print(df.isna())

Fill missing values

filled_df = df.fillna("Unknown")
print(filled_df)

Drop rows with missing values

cleaned_df = df.dropna()
print(cleaned_df)

Summary of Pandas Selection Methods

Selection Type	Syntax Example	Notes
Single Column	`df["column_name"]`	Access column as a Series.
Multiple Columns	`df[["col1", "col2"]]`	Returns a new DataFrame.
Single Row (label)	`df.loc[label]`	Label-based row selection.
Single Row (position)	`df.iloc[position]`	Position-based row selection.
Specific Rows and Columns	`df.loc[[rows], [columns]]`	Label-based row/column selection.
Filtering Rows	`df[condition]`	Filter rows based on conditions.

Matplotlib

Agenda

Learn the basics of the Matplotlib library.
Understand how to create various types of plots (line, bar, scatter, etc.).
Customize plots with titles, labels, legends, and styles.
Save plots as image files (PNG, PDF, SVG, EPS, and PGF.).

Introduction to Matplotlib

Matplotlib is a python 2D and 3D plotting library which produces scientific figures and publication quality figures.
The core component is the pyplot module, often imported as plt.
It is modeled closely after “Matlab™”. Therefore, the majority of plotting commands in pyplot have Matlab™ analogs with similar arguments.
You can create a wide variety of plots, including line plots, bar charts, histograms, scatter plots, and more.

Importing Matplotlib

# Import Matplotlib's pyplot module
import matplotlib.pyplot as plt

# Check the Matplotlib version
print(plt.__version__)

Line Plot

Creating a Simple Line Plot

# Data for the plot
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

# Create a line plot
plt.plot(x, y)
plt.title("Simple Line Plot")  # Add a title
plt.xlabel("X-axis")           # Add an X-axis label
plt.ylabel("Y-axis")           # Add a Y-axis label
plt.show()                     # Display the plot

Line Plot one

Customizing the Line Plot

# Customize line style, color, and markers
plt.plot(x, y, color="red", linestyle="--", marker="o", label="Data")
plt.title("Customized Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()  # Add a legend
plt.grid(True)  # Add grid lines
plt.show()

Line Plot two

Bar Chart

Creating a Bar Chart

# Data for the bar chart
categories = ["A", "B", "C", "D"]
values = [5, 7, 8, 6]

# Create a bar chart
plt.bar(categories, values, color="blue")
plt.title("Bar Chart")
plt.xlabel("Categories")
plt.ylabel("Values")
plt.show()

Bar Plot one

Horizontal Bar Chart

# Create a horizontal bar chart
plt.barh(categories, values, color="green")
plt.title("Horizontal Bar Chart")
plt.xlabel("Values")
plt.ylabel("Categories")
plt.show()

Bar Plot two

Scatter Plot

Creating a Scatter Plot

# Data for the scatter plot
x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]

# Create a scatter plot
plt.scatter(x, y, color="purple", marker="x")
plt.title("Scatter Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Scatter Plot two

Histogram

Creating a Histogram

# Data for the histogram
data = [22, 87, 5, 43, 56, 73, 55, 54, 11, 20, 51, 5, 79, 31, 27]

# Create a histogram
plt.hist(data, bins=5, color="orange", edgecolor="black")
plt.title("Histogram")
plt.xlabel("Data")
plt.ylabel("Frequency")
plt.show()

Histogram Plot two

Pie Chart

Creating a Pie Chart

# Data for the pie chart
sizes = [30, 20, 35, 15]
labels = ["Apples", "Bananas", "Cherries", "Dates"]

# Create a pie chart
plt.pie(sizes, labels=labels, autopct="%1.1f%%", startangle=90)
plt.title("Pie Chart")
plt.show()

Pie Plot two

Subplots

Subplots allow you to create multiple plots in a single figure.
Use plt.subplot() for simple grids and plt.subplots() for more control.

Creating Simple Subplots

import matplotlib.pyplot as plt
import numpy as np

# Data for plots
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Create subplot
plt.subplot(2, 1, 1)  # 2 rows, 1 column, 1st subplot
plt.plot(x, y1, label="sin(x)", color="blue")
plt.title("Sine Wave")
plt.legend()

plt.subplot(2, 1, 2)  # 2 rows, 1 column, 2nd subplot
plt.plot(x, y2, label="cos(x)", color="red")
plt.title("Cosine Wave")
plt.legend()

plt.tight_layout()  # Adjust layout to avoid overlapping
plt.show()

Subplots Plot two

Using plt.subplots() for More Control

# Create a figure with multiple subplots
fig, axes = plt.subplots(2, 2, figsize=(10, 8))

# Sine wave
axes[0, 0].plot(x, y1, color="blue")
axes[0, 0].set_title("Sine Wave")

# Cosine wave
axes[0, 1].plot(x, y2, color="red")
axes[0, 1].set_title("Cosine Wave")

# Exponential
y3 = np.exp(x / 10)
axes[1, 0].plot(x, y3, color="green")
axes[1, 0].set_title("Exponential Function")

# Logarithmic
y4 = np.log(x + 1)
axes[1, 1].plot(x, y4, color="purple")
axes[1, 1].set_title("Logarithmic Function")

# Add overall titles and adjust layout
fig.suptitle("Multiple Plots in One Figure", fontsize=16)
plt.tight_layout(rect=[0, 0, 1, 0.95])  # Adjust space for the title
plt.show()

Subplots Plot two

Generate and plot synthetic GDP data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Generate synthetic GDP data
np.random.seed(42)  # For reproducibility
years = np.arange(2000, 2023)  # 2000 to 2022
gdp = np.cumsum(np.random.uniform(0.5, 5, len(years))) + 100  # Cumulative GDP growth starting at 100

# Create a DataFrame
gdp_data = pd.DataFrame({"Year": years, "GDP": gdp})

# Plot the data
plt.figure(figsize=(10, 6))
plt.plot(gdp_data["Year"], gdp_data["GDP"], marker="o", linestyle="-", color="blue", label="GDP Growth")

# Adding plot details
plt.title("Annual GDP Growth (2000-2022)", fontsize=16)
plt.xlabel("Year", fontsize=12)
plt.ylabel("GDP (in Billion $)", fontsize=12)
plt.grid(True, linestyle="--", alpha=0.6)
plt.legend(fontsize=12)
plt.xticks(rotation=45)
plt.tight_layout()

# Show the plot
plt.show()

Display the GDP Plot

GDP Plot

3D Plots (Optional)

Matplotlib provides 3D plotting functionality through the mpl_toolkits.mplot3d module.
Use ax.plot_surface() for surface plots and ax.scatter() for 3D scatter plots.

3D Line Plot

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

# Data for a 3D line
z = np.linspace(0, 1, 100)
x = z * np.sin(25 * z)
y = z * np.cos(25 * z)

# Create a 3D plot
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection="3d")

ax.plot(x, y, z, label="3D Spiral", color="blue")
ax.set_title("3D Line Plot")
ax.set_xlabel("X-axis")
ax.set_ylabel("Y-axis")
ax.set_zlabel("Z-axis")
ax.legend()

plt.show()

3D Line Plot

3D Surface Plot

# Create data for a surface plot
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
x, y = np.meshgrid(x, y)
z = np.sin(np.sqrt(x**2 + y**2))

# Create a 3D surface plot
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection="3d")

surf = ax.plot_surface(x, y, z, cmap="viridis")
fig.colorbar(surf)  # Add a color bar for reference
ax.set_title("3D Surface Plot")
ax.set_xlabel("X-axis")
ax.set_ylabel("Y-axis")
ax.set_zlabel("Z-axis")

plt.show()

3D Surface Plot

3D Scatter Plot

# Create random 3D data
np.random.seed(42)
x = np.random.rand(50)
y = np.random.rand(50)
z = np.random.rand(50)

# Create a 3D scatter plot
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection="3d")

ax.scatter(x, y, z, color="red", marker="o", label="Data Points")
ax.set_title("3D Scatter Plot")
ax.set_xlabel("X-axis")
ax.set_ylabel("Y-axis")
ax.set_zlabel("Z-axis")
ax.legend()

plt.show()

3D Surface Plot

Saving a Plot

Saving a Plot as an Image

# Create a plot
plt.plot(x, y)
plt.title("Line Plot")

# Save the plot as a PNG file
plt.savefig("line_plot.png")

# Save the plot as a high-quality image
plt.savefig("line_plot_high_res.png", dpi=300)

# Display the plot
plt.show()

Key Points:

Matplotlib provides tools for creating a variety of plots and visualizations.
Use plt.plot() for line plots, plt.bar() for bar charts, plt.scatter() for scatter plots, and plt.hist() for histograms.
Customize plots with titles, axis labels, legends, and grid lines.
Save your plots using plt.savefig() in various formats.

Exercise

Numpy

1) Create a 1D array of integers from 5 to 15.
2) Create a 2D array of shape (3, 3) filled with ones.
3) Multiply all elements in the array [2, 4, 6, 8] by 3.
4) Add the arrays [1, 2, 3] and [4, 5, 6].
5) Access the last row of the array [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
6) Slice the array np.arange(10) to get only even numbers.
7) Reshape the array np.arange(12) into a (3, 4) array.
8) Flatten the reshaped array back into 1D.

Pandas

1) Create a Pandas Series with the following data: [100, 200, 300]. Assign the indices as [“X”, “Y”, “Z”].
2) Create a DataFrame with columns Product, Price, and Stock using the following data:

Products: [“Laptop”, “Tablet”, “Smartphone”] Prices: [1000, 500, 800] Stock: [50, 100, 200] Filtering and Sorting

3) Filter rows in the DataFrame where Price > 600.
4) Sort the DataFrame by Stock in ascending order. 5) Create a DataFrame with some missing values and write code to:

Replace missing values with “Not Available”.
Remove rows with missing values.

Matplotlib

1) Create a line plot for x = [1, 2, 3, 4, 5] and y = [10, 20, 25, 30, 35]. Customize the plot by changing the line color, adding markers, and a legend.
2) Create a bar chart for categories [“Math”, “Science”, “History”, “English”] and values [85, 90, 75, 80]. Add a title, and X and Y axis labels.
3) Plot x = [10, 20, 30, 40, 50] and y = [5, 15, 25, 35, 45] as a scatter plot. Change the marker style and color.
4) Create a histogram for the dataset [5, 7, 8, 9, 5, 3, 4, 5, 7, 8, 9, 10] with 4 bins.
5) Create a pie chart for sizes = [40, 30, 20, 10] with labels [“A”, “B”, “C”, “D”]
6) Import your annual GDP data into a Pandas DataFrame and visualize the data using Matplotlib.

Key Points

NumPy provides fast, efficient, and flexible array operations.

Pandas is a powerful library for data manipulation and analysis.

Matplotlib is a python 2D and 3D plotting library which produces scientific figures and publication quality figures.

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Python Core Libraries

Overview

Python Core Libraries

NumPy

Agenda

Introduction to NumPy

Import the NumPy library

Creating NumPy Arrays

Creating Arrays from Lists

Create a 1D array

Create a 2D array

Create a 3D Arrays

Creating Arrays with NumPy Functions

Create an array of zeros

Create an array of ones

Random numbers

Create an array with a range of numbers

Create an array with evenly spaced numbers

Basic Array Operations

Mathematical Operations

Perform basic math operations

Perform element-wise operations with another array

Array Properties

Get properties of an array

Indexing and Slicing

Indexing

Access elements of a 1D array

Access elements of a 2D array

Slicing

Slice a 1D array

Slice a 2D array

Reshaping Arrays

Changing the Shape of an Array

Reshape a 1D array to a 2D array

Flatten a 2D array back to 1D

Broadcasting

Add a scalar to a 2D array

Add a 1D array to a 2D array

Asking For Help

Key Points:

Pandas

Agenda

Introduction to Pandas

Import the Pandas library

Pandas Series

Pandas DataFrame

Create a DataFrame from a dictionary

Viewing DataFrame

Print the head of the DataFrame

Print the tail of the DataFrame

Display DataFrame information

Display the data types

Display statistical summary of DataFrame

Display the DataFrame index and columns

Get the DataFrame index

Get the DataFrame columns

Accessing a DataFrame

Adding New Columns

Dropping Columns

Dropping Rows

Reading and Writing Data

Read data from a CSV file

Write data to a CSV file

Reading Excel Files

Writing Excel Files

Writing Multiple Sheets

Reading JSON Files

Writing JSON Files

Data Manipulation

Filtering

Sort by Age in descending order

Aggregations

Selecting Columns and Rows in Pandas

Selecting Columns

Selecting a Single Column

Selecting Multiple Columns

Renaming Columns