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Multiple filtering pandas columns based on values in another column
Creating new columns by iterating over rows in pandas dataframePandas - Get feature values which appear in two distinct dataframesPandas Query Optimization On Multiple Columnshow many rows have values from the same columns pandasExport pandas to dictionary by combining multiple row valuesCombine Pandas DataFrames with year columnsSpearmanr on two pandas dataframesShould I use pandas get_dummies and create additional columns or use my own encoding code that keeps 1 column?Merging common Columns values in two DataFrame PandasAggregate values of same name pandas dataframe columns to single column
$begingroup$
I have a pandas dataframe df1:
Now, I want to filter the rows in df1 based on unique combinations of (Campaign, Merchant) from another dataframe, df2, which look like this:
What I tried is using .isin, with a code similar to the one below:
df1.loc[df1['Campaign'].isin(df2['Campaign']) &
df1['Merchant'].isin(df2['Merchant'])]
The problem here is that the conditions are independent eg : I want to check if (A,1) from df2 is in df1, but with the above condition, since I am checking all the list, not row by row, it would return all rows in df1 where Campaign column is A OR Merchant column is 1.
Do you have any suggestion for this multiple pandas filtering?
python pandas
edited 2 days ago
tuomastik
753418
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
$endgroup$
add a comment |
$begingroup$
I have a pandas dataframe df1:
Now, I want to filter the rows in df1 based on unique combinations of (Campaign, Merchant) from another dataframe, df2, which look like this:
What I tried is using .isin, with a code similar to the one below:
df1.loc[df1['Campaign'].isin(df2['Campaign']) &
df1['Merchant'].isin(df2['Merchant'])]
The problem here is that the conditions are independent eg : I want to check if (A,1) from df2 is in df1, but with the above condition, since I am checking all the list, not row by row, it would return all rows in df1 where Campaign column is A OR Merchant column is 1.
Do you have any suggestion for this multiple pandas filtering?
python pandas
edited 2 days ago
tuomastik
753418
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
$endgroup$
add a comment |
$begingroup$
I have a pandas dataframe df1:
Now, I want to filter the rows in df1 based on unique combinations of (Campaign, Merchant) from another dataframe, df2, which look like this:
What I tried is using .isin, with a code similar to the one below:
df1.loc[df1['Campaign'].isin(df2['Campaign']) &
df1['Merchant'].isin(df2['Merchant'])]
The problem here is that the conditions are independent eg : I want to check if (A,1) from df2 is in df1, but with the above condition, since I am checking all the list, not row by row, it would return all rows in df1 where Campaign column is A OR Merchant column is 1.
Do you have any suggestion for this multiple pandas filtering?
python pandas
edited 2 days ago
tuomastik
753418
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
$endgroup$
I have a pandas dataframe df1:
Now, I want to filter the rows in df1 based on unique combinations of (Campaign, Merchant) from another dataframe, df2, which look like this:
What I tried is using .isin, with a code similar to the one below:
df1.loc[df1['Campaign'].isin(df2['Campaign']) &
df1['Merchant'].isin(df2['Merchant'])]
The problem here is that the conditions are independent eg : I want to check if (A,1) from df2 is in df1, but with the above condition, since I am checking all the list, not row by row, it would return all rows in df1 where Campaign column is A OR Merchant column is 1.
Do you have any suggestion for this multiple pandas filtering?
python pandas
python pandas
edited 2 days ago
tuomastik
753418
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
edited 2 days ago
tuomastik
753418
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
edited 2 days ago
tuomastik
753418
edited 2 days ago
tuomastik
753418
edited 2 days ago
tuomastik
753418
753418
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
asked Mar 18 at 21:25
Remus RaphaelRemus Raphael
112
112
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
import pandas as pd
df1 = pd.DataFrame("Random numbers 1": pd.np.random.randn(6),
"Campaign": ["A"] * 5 + ["B"],
"Merchant": [1, 1, 1, 2, 3, 1])
df2 = pd.DataFrame("Random numbers 2": pd.np.random.randn(6),
"Campaign": ["A"] * 2 + ["B"] * 2 + ["C"] * 2,
"Merchant": [1, 2, 1, 2, 1, 2])
columns_consider = ["Campaign", "Merchant"]
combined = pd.concat((df1[columns_consider].drop_duplicates(),
df2[columns_consider].drop_duplicates()), ignore_index=True)
identical = combined[combined.duplicated()]
print(identical)
Output:
Campaign Merchant
4 A 1
5 A 2
6 B 1
answered 2 days ago
tuomastiktuomastik
753418
$endgroup$
add a comment |
Your Answer
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
import pandas as pd
df1 = pd.DataFrame("Random numbers 1": pd.np.random.randn(6),
"Campaign": ["A"] * 5 + ["B"],
"Merchant": [1, 1, 1, 2, 3, 1])
df2 = pd.DataFrame("Random numbers 2": pd.np.random.randn(6),
"Campaign": ["A"] * 2 + ["B"] * 2 + ["C"] * 2,
"Merchant": [1, 2, 1, 2, 1, 2])
columns_consider = ["Campaign", "Merchant"]
combined = pd.concat((df1[columns_consider].drop_duplicates(),
df2[columns_consider].drop_duplicates()), ignore_index=True)
identical = combined[combined.duplicated()]
print(identical)
Output:
Campaign Merchant
4 A 1
5 A 2
6 B 1
answered 2 days ago
tuomastiktuomastik
753418
$endgroup$
add a comment |
$begingroup$
import pandas as pd
df1 = pd.DataFrame("Random numbers 1": pd.np.random.randn(6),
"Campaign": ["A"] * 5 + ["B"],
"Merchant": [1, 1, 1, 2, 3, 1])
df2 = pd.DataFrame("Random numbers 2": pd.np.random.randn(6),
"Campaign": ["A"] * 2 + ["B"] * 2 + ["C"] * 2,
"Merchant": [1, 2, 1, 2, 1, 2])
columns_consider = ["Campaign", "Merchant"]
combined = pd.concat((df1[columns_consider].drop_duplicates(),
df2[columns_consider].drop_duplicates()), ignore_index=True)
identical = combined[combined.duplicated()]
print(identical)
Output:
Campaign Merchant
4 A 1
5 A 2
6 B 1
answered 2 days ago
tuomastiktuomastik
753418
$endgroup$
add a comment |
$begingroup$
import pandas as pd
df1 = pd.DataFrame("Random numbers 1": pd.np.random.randn(6),
"Campaign": ["A"] * 5 + ["B"],
"Merchant": [1, 1, 1, 2, 3, 1])
df2 = pd.DataFrame("Random numbers 2": pd.np.random.randn(6),
"Campaign": ["A"] * 2 + ["B"] * 2 + ["C"] * 2,
"Merchant": [1, 2, 1, 2, 1, 2])
columns_consider = ["Campaign", "Merchant"]
combined = pd.concat((df1[columns_consider].drop_duplicates(),
df2[columns_consider].drop_duplicates()), ignore_index=True)
identical = combined[combined.duplicated()]
print(identical)
Output:
Campaign Merchant
4 A 1
5 A 2
6 B 1
answered 2 days ago
tuomastiktuomastik
753418
$endgroup$
import pandas as pd
df1 = pd.DataFrame("Random numbers 1": pd.np.random.randn(6),
"Campaign": ["A"] * 5 + ["B"],
"Merchant": [1, 1, 1, 2, 3, 1])
df2 = pd.DataFrame("Random numbers 2": pd.np.random.randn(6),
"Campaign": ["A"] * 2 + ["B"] * 2 + ["C"] * 2,
"Merchant": [1, 2, 1, 2, 1, 2])
columns_consider = ["Campaign", "Merchant"]
combined = pd.concat((df1[columns_consider].drop_duplicates(),
df2[columns_consider].drop_duplicates()), ignore_index=True)
identical = combined[combined.duplicated()]
print(identical)
Output:
Campaign Merchant
4 A 1
5 A 2
6 B 1
answered 2 days ago
tuomastiktuomastik
753418
answered 2 days ago
tuomastiktuomastik
753418
answered 2 days ago
tuomastiktuomastik
753418
answered 2 days ago
tuomastiktuomastik
753418
753418
add a comment |
add a comment |
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