Pandas DataFrame : selection of multiple elements in several columns
I have this Python Pandas DataFrame DF :
DICT = { 'letter': ['A','B','C','A','B','C','A','B','C'],
'number': [1,1,1,2,2,2,3,3,3],
'word' : ['one','two','three','three','two','one','two','one','three']}
DF = pd.DataFrame(DICT)
Which looks like :
letter number word
0 A 1 one
1 B 1 two
2 C 1 three
3 A 2 three
4 B 2 two
5 C 2 one
6 A 3 two
7 B 3 one
8 C 3 three
And I want to extract the lines
letter number word
A 1 one
B 2 two
C 3 three
First I tired :
DF[(DF['letter'].isin(("A","B","C"))) &
DF['number'].isin((1,2,3)) &
DF['word'].isin(('one','two','three'))]
Of course it didn't work, and everything has been selected
Then I tested :
Bool = DF[['letter','number','word']].isin(("A",1,"one"))
DF[np.all(Bool,axis=1)]
Good, it works ! but only for one line ...
If we take the next step and give an iterable to .isin() :
Bool = DF[['letter','number','word']].isin((("A",1,"one"),
("B",2,"two"),
("C",3,"three")))
Then it fails, the Boolean array is full of False ...
What I'm doing wrong ? Is there a more elegant way to do this selection based on several columns ?
(Anyway, I want to avoid a for loop, because the real DataFrames I'm using are really big, so I'm looking for the fastest optimal way to do the job)
python pandas dataframe multiple-columns selection
asked Nov 22 at 13:27
Covich
817922
add a comment |
I have this Python Pandas DataFrame DF :
DICT = { 'letter': ['A','B','C','A','B','C','A','B','C'],
'number': [1,1,1,2,2,2,3,3,3],
'word' : ['one','two','three','three','two','one','two','one','three']}
DF = pd.DataFrame(DICT)
Which looks like :
letter number word
0 A 1 one
1 B 1 two
2 C 1 three
3 A 2 three
4 B 2 two
5 C 2 one
6 A 3 two
7 B 3 one
8 C 3 three
And I want to extract the lines
letter number word
A 1 one
B 2 two
C 3 three
First I tired :
DF[(DF['letter'].isin(("A","B","C"))) &
DF['number'].isin((1,2,3)) &
DF['word'].isin(('one','two','three'))]
Of course it didn't work, and everything has been selected
Then I tested :
Bool = DF[['letter','number','word']].isin(("A",1,"one"))
DF[np.all(Bool,axis=1)]
Good, it works ! but only for one line ...
If we take the next step and give an iterable to .isin() :
Bool = DF[['letter','number','word']].isin((("A",1,"one"),
("B",2,"two"),
("C",3,"three")))
Then it fails, the Boolean array is full of False ...
What I'm doing wrong ? Is there a more elegant way to do this selection based on several columns ?
(Anyway, I want to avoid a for loop, because the real DataFrames I'm using are really big, so I'm looking for the fastest optimal way to do the job)
python pandas dataframe multiple-columns selection
asked Nov 22 at 13:27
Covich
817922
add a comment |
I have this Python Pandas DataFrame DF :
DICT = { 'letter': ['A','B','C','A','B','C','A','B','C'],
'number': [1,1,1,2,2,2,3,3,3],
'word' : ['one','two','three','three','two','one','two','one','three']}
DF = pd.DataFrame(DICT)
Which looks like :
letter number word
0 A 1 one
1 B 1 two
2 C 1 three
3 A 2 three
4 B 2 two
5 C 2 one
6 A 3 two
7 B 3 one
8 C 3 three
And I want to extract the lines
letter number word
A 1 one
B 2 two
C 3 three
First I tired :
DF[(DF['letter'].isin(("A","B","C"))) &
DF['number'].isin((1,2,3)) &
DF['word'].isin(('one','two','three'))]
Of course it didn't work, and everything has been selected
Then I tested :
Bool = DF[['letter','number','word']].isin(("A",1,"one"))
DF[np.all(Bool,axis=1)]
Good, it works ! but only for one line ...
If we take the next step and give an iterable to .isin() :
Bool = DF[['letter','number','word']].isin((("A",1,"one"),
("B",2,"two"),
("C",3,"three")))
Then it fails, the Boolean array is full of False ...
What I'm doing wrong ? Is there a more elegant way to do this selection based on several columns ?
(Anyway, I want to avoid a for loop, because the real DataFrames I'm using are really big, so I'm looking for the fastest optimal way to do the job)
python pandas dataframe multiple-columns selection
asked Nov 22 at 13:27
Covich
817922
I have this Python Pandas DataFrame DF :
DICT = { 'letter': ['A','B','C','A','B','C','A','B','C'],
'number': [1,1,1,2,2,2,3,3,3],
'word' : ['one','two','three','three','two','one','two','one','three']}
DF = pd.DataFrame(DICT)
Which looks like :
letter number word
0 A 1 one
1 B 1 two
2 C 1 three
3 A 2 three
4 B 2 two
5 C 2 one
6 A 3 two
7 B 3 one
8 C 3 three
And I want to extract the lines
letter number word
A 1 one
B 2 two
C 3 three
First I tired :
DF[(DF['letter'].isin(("A","B","C"))) &
DF['number'].isin((1,2,3)) &
DF['word'].isin(('one','two','three'))]
Of course it didn't work, and everything has been selected
Then I tested :
Bool = DF[['letter','number','word']].isin(("A",1,"one"))
DF[np.all(Bool,axis=1)]
Good, it works ! but only for one line ...
If we take the next step and give an iterable to .isin() :
Bool = DF[['letter','number','word']].isin((("A",1,"one"),
("B",2,"two"),
("C",3,"three")))
Then it fails, the Boolean array is full of False ...
What I'm doing wrong ? Is there a more elegant way to do this selection based on several columns ?
(Anyway, I want to avoid a for loop, because the real DataFrames I'm using are really big, so I'm looking for the fastest optimal way to do the job)
python pandas dataframe multiple-columns selection
python pandas dataframe multiple-columns selection
asked Nov 22 at 13:27
Covich
817922
asked Nov 22 at 13:27
Covich
817922
asked Nov 22 at 13:27
Covich
817922
asked Nov 22 at 13:27
Covich
817922
asked Nov 22 at 13:27
Covich
817922
817922
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Idea is create new DataFrame with all triple values and then merge with original DataFrame:
L = [("A",1,"one"),
("B",2,"two"),
("C",3,"three")]
df1 = pd.DataFrame(L, columns=['letter','number','word'])
print (df1)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
df = DF.merge(df1)
print (df)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
Another idea is create list of tuples, convert to Series and then compare by isin:
s = pd.Series(list(map(tuple, DF[['letter','number','word']].values.tolist())),index=DF.index)
df1 = DF[s.isin(L)]
print (df1)
letter number word
0 A 1 one
4 B 2 two
8 C 3 three
answered Nov 22 at 13:29
jezrael
319k22258337
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
Idea is create new DataFrame with all triple values and then merge with original DataFrame:
L = [("A",1,"one"),
("B",2,"two"),
("C",3,"three")]
df1 = pd.DataFrame(L, columns=['letter','number','word'])
print (df1)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
df = DF.merge(df1)
print (df)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
Another idea is create list of tuples, convert to Series and then compare by isin:
s = pd.Series(list(map(tuple, DF[['letter','number','word']].values.tolist())),index=DF.index)
df1 = DF[s.isin(L)]
print (df1)
letter number word
0 A 1 one
4 B 2 two
8 C 3 three
answered Nov 22 at 13:29
jezrael
319k22258337
add a comment |
Idea is create new DataFrame with all triple values and then merge with original DataFrame:
L = [("A",1,"one"),
("B",2,"two"),
("C",3,"three")]
df1 = pd.DataFrame(L, columns=['letter','number','word'])
print (df1)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
df = DF.merge(df1)
print (df)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
Another idea is create list of tuples, convert to Series and then compare by isin:
s = pd.Series(list(map(tuple, DF[['letter','number','word']].values.tolist())),index=DF.index)
df1 = DF[s.isin(L)]
print (df1)
letter number word
0 A 1 one
4 B 2 two
8 C 3 three
answered Nov 22 at 13:29
jezrael
319k22258337
add a comment |
Idea is create new DataFrame with all triple values and then merge with original DataFrame:
L = [("A",1,"one"),
("B",2,"two"),
("C",3,"three")]
df1 = pd.DataFrame(L, columns=['letter','number','word'])
print (df1)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
df = DF.merge(df1)
print (df)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
Another idea is create list of tuples, convert to Series and then compare by isin:
s = pd.Series(list(map(tuple, DF[['letter','number','word']].values.tolist())),index=DF.index)
df1 = DF[s.isin(L)]
print (df1)
letter number word
0 A 1 one
4 B 2 two
8 C 3 three
answered Nov 22 at 13:29
jezrael
319k22258337
Idea is create new DataFrame with all triple values and then merge with original DataFrame:
L = [("A",1,"one"),
("B",2,"two"),
("C",3,"three")]
df1 = pd.DataFrame(L, columns=['letter','number','word'])
print (df1)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
df = DF.merge(df1)
print (df)
letter number word
0 A 1 one
1 B 2 two
2 C 3 three
Another idea is create list of tuples, convert to Series and then compare by isin:
s = pd.Series(list(map(tuple, DF[['letter','number','word']].values.tolist())),index=DF.index)
df1 = DF[s.isin(L)]
print (df1)
letter number word
0 A 1 one
4 B 2 two
8 C 3 three
answered Nov 22 at 13:29
jezrael
319k22258337
answered Nov 22 at 13:29
jezrael
319k22258337
answered Nov 22 at 13:29
jezrael
319k22258337
answered Nov 22 at 13:29
jezrael
319k22258337
319k22258337
add a comment |
add a comment |
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