Filter Pandas Dataframe by Row Elements in Another Dataframe

When working with pandas dataframes, it is common to need to filter one dataframe based on the elements in another. In this case, we want to filter our main dataframe based on the values in a second dataframe.

To accomplish this, we can use the `isin()` function in pandas. This function allows us to check if the values in a column of our main dataframe are present in a list of values from our second dataframe. We can then filter our main dataframe based on this check.

Here's an example:


import pandas as pd

# create our main dataframe
df_main = pd.DataFrame({'fruit': ['apple', 'banana', 'orange', 'grape'], 'color': ['red', 'yellow', 'orange', 'purple']})

# create our second dataframe with the values we want to filter by
df_filter = pd.DataFrame({'fruit': ['apple', 'orange']})

# use the isin() function to check if the values in the 'fruit' column of df_main are present in df_filter
mask = df_main['fruit'].isin(df_filter['fruit'].tolist())

# filter df_main based on the mask
filtered_df = df_main[mask]

In this example, we create a main dataframe with two columns ('fruit' and 'color'), and a second dataframe with a single column ('fruit') containing the values we want to filter by ('apple' and 'orange'). We then use the `isin()` function to create a mask that checks if the values in the 'fruit' column of df_main are present in df_filter. Finally, we filter df_main based on this mask using boolean indexing.

Using the `isin()` function is a simple and efficient way to filter pandas dataframes based on the elements in another dataframe.

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