Here are examples of merging Pandas DataFrames using inner joins and left joins. Let's start by creating the dataframes and data in them.

import pandas as pd

# Create the first DataFrame
df1 = pd.DataFrame({'ID': [1, 2, 3, 4, 5],
                    'Name': ['John', 'Alice', 'Bob', 'Charlie', 'Eve']})

# Create the second DataFrame
df2 = pd.DataFrame({'ID': [2, 3, 4, 6, 7],
                    'Age': [25, 32, 47, 31, 19]})

# Print the first DataFrame
print("DataFrame 1:")
print(df1)
print()

# Print the second DataFrame
print("DataFrame 2:")
print(df2)
print()

Output:

DataFrame 1:
   ID     Name
0   1     John
1   2    Alice
2   3      Bob
3   4  Charlie
4   5      Eve

DataFrame 2:
   ID  Age
0   2   25
1   3   32
2   4   47
3   6   31
4   7   19

Now, let's perform the inner join and left join operations.

Inner Join

An inner join returns only the matching rows between two DataFrames based on a common column.

# Perform the inner join
inner_join_df = pd.merge(df1, df2, on='ID', how='inner')

# Print the inner join DataFrame
print("Inner Join:")
print(inner_join_df)

Output:

Inner Join:
   ID   Name  Age
0   2  Alice   25
1   3    Bob   32
2   4    Eve   47

Left Join

A left join returns all the rows from the left DataFrame and the matching rows from the right DataFrame. If there is no match, it returns NaN values.

# Perform the left join
left_join_df = pd.merge(df1, df2, on='ID', how='left')

# Print the left join DataFrame
print("Left Join:")
print(left_join_df)

Output:

Left Join:
   ID     Name   Age
0   1     John   NaN
1   2    Alice  25.0
2   3      Bob  32.0
3   4  Charlie  47.0
4   5      Eve   NaN

In the left join example, notice that the resulting DataFrame contains all the rows from df1 and the matching rows from df2. The Age column for non-matching rows is filled with NaN values.

I hope this helps! Let me know if you have any further questions.