Multiple filtering pandas columns based on values in another 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 18 hours ago
tuomastik
751418
asked yesterday
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 18 hours ago
tuomastik
751418
asked yesterday
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 18 hours ago
tuomastik
751418
asked yesterday
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 18 hours ago
tuomastik
751418
asked yesterday
Remus RaphaelRemus Raphael
112
edited 18 hours ago
tuomastik
751418
asked yesterday
Remus RaphaelRemus Raphael
112
edited 18 hours ago
tuomastik
751418
edited 18 hours ago
tuomastik
751418
edited 18 hours ago
tuomastik
751418
751418
asked yesterday
Remus RaphaelRemus Raphael
112
asked yesterday
Remus RaphaelRemus Raphael
112
asked yesterday
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 23 hours ago
tuomastiktuomastik
751418
$endgroup$
add a comment |
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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 23 hours ago
tuomastiktuomastik
751418
$endgroup$
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 23 hours ago
tuomastiktuomastik
751418
$endgroup$
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 23 hours ago
tuomastiktuomastik
751418
$endgroup$
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 23 hours ago
tuomastiktuomastik
751418
answered 23 hours ago
tuomastiktuomastik
751418
answered 23 hours ago
tuomastiktuomastik
751418
answered 23 hours ago
tuomastiktuomastik
751418
751418
add a comment |
add a comment |
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