# 1 to 2 matching in two dataframes with different sizes in python r

When working with dataframes in Python, it is common to encounter situations where you need to match values between two dataframes. This can be particularly challenging when the dataframes have different sizes. In this article, we will explore three different ways to solve the problem of matching values between two dataframes with different sizes in Python.

## Method 1: Using merge()

The first method involves using the merge() function from the pandas library. This function allows us to combine two dataframes based on a common column or index. To solve the problem of matching values between two dataframes with different sizes, we can use the merge() function with the ‘inner’ option, which will only include the matching rows.

``````
import pandas as pd

# Create two sample dataframes
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df2 = pd.DataFrame({'A': [1, 2], 'C': ['x', 'y']})

# Merge the dataframes based on column 'A'
merged_df = pd.merge(df1, df2, on='A', how='inner')

# Print the merged dataframe
print(merged_df)
``````

This code will output:

``````
A  B  C
0  1  a  x
1  2  b  y
``````

## Method 2: Using isin()

The second method involves using the isin() function from the pandas library. This function allows us to check if values from one dataframe are present in another dataframe. To solve the problem of matching values between two dataframes with different sizes, we can use the isin() function to create a boolean mask and filter the rows accordingly.

``````
import pandas as pd

# Create two sample dataframes
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df2 = pd.DataFrame({'A': [1, 2], 'C': ['x', 'y']})

# Create a boolean mask based on column 'A'

# Filter the rows based on the boolean mask

# Print the filtered dataframe
print(filtered_df)
``````

This code will output:

``````
A  B
0  1  a
1  2  b
``````

## Method 3: Using merge() and dropna()

The third method involves using both the merge() and dropna() functions from the pandas library. This method is useful when we want to keep all the rows from one dataframe and only include the matching rows from the other dataframe. To solve the problem of matching values between two dataframes with different sizes, we can merge the dataframes and then drop the rows with missing values.

``````
import pandas as pd

# Create two sample dataframes
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df2 = pd.DataFrame({'A': [1, 2], 'C': ['x', 'y']})

# Merge the dataframes based on column 'A'
merged_df = pd.merge(df1, df2, on='A', how='left')

# Drop the rows with missing values
filtered_df = merged_df.dropna()

# Print the filtered dataframe
print(filtered_df)
``````

This code will output:

``````
A  B  C
0  1  a  x
1  2  b  y
``````

After exploring these three different methods, it is clear that the best option depends on the specific requirements of your problem. If you want to keep only the matching rows, Method 1 using merge() is the most suitable. If you want to filter the rows based on a condition, Method 2 using isin() is a good choice. Finally, if you want to keep all the rows from one dataframe and only include the matching rows from the other dataframe, Method 3 using merge() and dropna() is the way to go.

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