How to delete all rows from pandas dataframe1 that do NOT exist in pandas dataframe2
Question:
I have two pandas dataframes, data1 and data2. They each have album and artist columns along with other columns that are different attributes. For the sake of what I’m trying to do, I want to delete all of the rows in data2 that DO NOT exist in data1. So, essentially I want all of the album and artists in data2 to match data1. Does anyone know the right way to go about this in python? TIA!
So far I’ve tried:
data2 = data2[data2[‘album’, ‘artist’].isin(data1[‘album’, ‘artist’])]
but it doesn’t like the ‘,’ to get both attributes to match.
Answers:
To remove all rows from a dataframe that do not exist in another dataframe, you can use the merge() method from pandas, along with the indicator parameter. The indicator parameter allows you to specify whether you want to keep only the rows that exist in both dataframes (the default behavior), only the rows that exist in the left dataframe, only the rows that exist in the right dataframe, or all rows from both dataframes.
For example, to remove all rows from data1 that do not exist in data2, you can use the merge() method with the indicator parameter set to ‘right_only’, like this:
# Merge data1 and data2 on the 'album' and 'artist' columns
merged_data = data1.merge(data2, on=['album', 'artist'], indicator=True)
# Keep only the rows where the _merge column is 'right_only'
merged_data = merged_data[merged_data['_merge'] == 'right_only']
# Drop the _merge column
merged_data = merged_data.drop('_merge', axis=1)
# Print the first few rows of the merged dataframe
print(merged_data.head())
This will create a new dataframe called merged_data that contains only the rows from data1 that do not exist in data2. The _merge column indicates whether the row exists in both dataframes (‘both’), only in the left dataframe (‘left_only’), only in the right dataframe (‘right_only’), or in neither dataframe (‘neither’). In this case, we use the _merge column to filter the dataframe and keep only the rows that have a value of ‘right_only’. Then, we drop the _merge column from the dataframe, since it is no longer needed.
You can use the merge method in Pandas to join the two dataframes on the album and artist columns and keep only the rows that exist in both dataframes. Here is an example of how you could do this:
import pandas as pd
# Create some sample dataframes
data1 = pd.DataFrame({
"album": ["Thriller", "Back in Black", "The Dark Side of the Moon"],
"artist": ["Michael Jackson", "AC/DC", "Pink Floyd"],
"year": [1982, 1980, 1973]
})
data2 = pd.DataFrame({
"album": ["The Bodyguard", "Thriller", "The Dark Side of the Moon"],
"artist": ["Whitney Houston", "Michael Jackson", "Pink Floyd"],
"genre": ["Soundtrack", "Pop", "Rock"]
})
# Merge the dataframes on the album and artist columns, and keep only the rows that exist in both dataframes
merged_data = data1.merge(data2, on=["album", "artist"], how="inner")
# Print the result
print(merged_data)
This code will print the following dataframe:
album artist year genre
0 Thriller Michael Jackson 1982 Pop
1 The Dark Side of the Moon Pink Floyd 1973 Rock
As you can see, this dataframe only contains the rows that exist in both data1 and data2. You can then use this dataframe instead of data2 to work with the rows that exist in both dataframes.
Note that the merge method will also join the columns from the two dataframes, so you may need to drop any unnecessary columns or rename columns with the same name to avoid conflicts. You can do this using the drop and rename methods in Pandas, respectively. For example:
# Drop the "genre" column from the merged dataframe
merged_data = merged_data.drop("genre", axis=1)
# Rename the "year" column in the merged dataframe
merged_data = merged_data.rename({"year": "release_year"}, axis=1)
# Print the result
print(merged_data)
This code will print the following dataframe:
album artist
May be this solves your case:
# First, create a new column that concatenates the album and artist columns in data1
data1['combo'] = data1['album'] + data1['artist']
# Repeat this for data2
data2['combo'] = data2['album'] + data2['artist']
# Next, keep only the rows in data2 where the combo column exists in data1
data2 = data2[data2['combo'].isin(data1['combo'])]
# Finally, drop the combo column from both dataframes
data1.drop(columns=['combo'], inplace=True)
data2.drop(columns=['combo'], inplace=True)
This approach creates a new column in each dataframe that concatenates the album and artist columns, and then uses the isin method to keep only the rows in data2 where the combo column exists in data1. The combo columns are then dropped from both dataframes.
Note that this approach assumes that there are no duplicate rows in either dataframe. If there are duplicate rows, you may need to use a different approach, such as grouping by the combo column and then keeping only groups that exist in both dataframes.
I have two pandas dataframes, data1 and data2. They each have album and artist columns along with other columns that are different attributes. For the sake of what I’m trying to do, I want to delete all of the rows in data2 that DO NOT exist in data1. So, essentially I want all of the album and artists in data2 to match data1. Does anyone know the right way to go about this in python? TIA!
So far I’ve tried:
data2 = data2[data2[‘album’, ‘artist’].isin(data1[‘album’, ‘artist’])]
but it doesn’t like the ‘,’ to get both attributes to match.
To remove all rows from a dataframe that do not exist in another dataframe, you can use the merge() method from pandas, along with the indicator parameter. The indicator parameter allows you to specify whether you want to keep only the rows that exist in both dataframes (the default behavior), only the rows that exist in the left dataframe, only the rows that exist in the right dataframe, or all rows from both dataframes.
For example, to remove all rows from data1 that do not exist in data2, you can use the merge() method with the indicator parameter set to ‘right_only’, like this:
# Merge data1 and data2 on the 'album' and 'artist' columns
merged_data = data1.merge(data2, on=['album', 'artist'], indicator=True)
# Keep only the rows where the _merge column is 'right_only'
merged_data = merged_data[merged_data['_merge'] == 'right_only']
# Drop the _merge column
merged_data = merged_data.drop('_merge', axis=1)
# Print the first few rows of the merged dataframe
print(merged_data.head())
This will create a new dataframe called merged_data that contains only the rows from data1 that do not exist in data2. The _merge column indicates whether the row exists in both dataframes (‘both’), only in the left dataframe (‘left_only’), only in the right dataframe (‘right_only’), or in neither dataframe (‘neither’). In this case, we use the _merge column to filter the dataframe and keep only the rows that have a value of ‘right_only’. Then, we drop the _merge column from the dataframe, since it is no longer needed.
You can use the merge method in Pandas to join the two dataframes on the album and artist columns and keep only the rows that exist in both dataframes. Here is an example of how you could do this:
import pandas as pd
# Create some sample dataframes
data1 = pd.DataFrame({
"album": ["Thriller", "Back in Black", "The Dark Side of the Moon"],
"artist": ["Michael Jackson", "AC/DC", "Pink Floyd"],
"year": [1982, 1980, 1973]
})
data2 = pd.DataFrame({
"album": ["The Bodyguard", "Thriller", "The Dark Side of the Moon"],
"artist": ["Whitney Houston", "Michael Jackson", "Pink Floyd"],
"genre": ["Soundtrack", "Pop", "Rock"]
})
# Merge the dataframes on the album and artist columns, and keep only the rows that exist in both dataframes
merged_data = data1.merge(data2, on=["album", "artist"], how="inner")
# Print the result
print(merged_data)
This code will print the following dataframe:
album artist year genre
0 Thriller Michael Jackson 1982 Pop
1 The Dark Side of the Moon Pink Floyd 1973 Rock
As you can see, this dataframe only contains the rows that exist in both data1 and data2. You can then use this dataframe instead of data2 to work with the rows that exist in both dataframes.
Note that the merge method will also join the columns from the two dataframes, so you may need to drop any unnecessary columns or rename columns with the same name to avoid conflicts. You can do this using the drop and rename methods in Pandas, respectively. For example:
# Drop the "genre" column from the merged dataframe
merged_data = merged_data.drop("genre", axis=1)
# Rename the "year" column in the merged dataframe
merged_data = merged_data.rename({"year": "release_year"}, axis=1)
# Print the result
print(merged_data)
This code will print the following dataframe:
album artist
May be this solves your case:
# First, create a new column that concatenates the album and artist columns in data1
data1['combo'] = data1['album'] + data1['artist']
# Repeat this for data2
data2['combo'] = data2['album'] + data2['artist']
# Next, keep only the rows in data2 where the combo column exists in data1
data2 = data2[data2['combo'].isin(data1['combo'])]
# Finally, drop the combo column from both dataframes
data1.drop(columns=['combo'], inplace=True)
data2.drop(columns=['combo'], inplace=True)
This approach creates a new column in each dataframe that concatenates the album and artist columns, and then uses the isin method to keep only the rows in data2 where the combo column exists in data1. The combo columns are then dropped from both dataframes.
Note that this approach assumes that there are no duplicate rows in either dataframe. If there are duplicate rows, you may need to use a different approach, such as grouping by the combo column and then keeping only groups that exist in both dataframes.