How to join Panda DataFrames based on List values in a column [duplicate]
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This question already has an answer here:
How to unnest (explode) a column in a pandas DataFrame?
6 answers
There are two Pandas DataFrame
df_A = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
col1 col2
r1 [a, b]
r2 [aabb, b]
r3 [xyz]
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
C1 C2
a 10
b 2
I want to join both dataframes such as df_C is
col1 C1 C2
r1 a 10
r1 b 2
r2 aabb 0
r2 b 2
r3 xyz 0
python pandas
marked as duplicate by Sandeep Kadapa, cs95
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Nov 17 '18 at 8:04
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
add a comment |
This question already has an answer here:
How to unnest (explode) a column in a pandas DataFrame?
6 answers
There are two Pandas DataFrame
df_A = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
col1 col2
r1 [a, b]
r2 [aabb, b]
r3 [xyz]
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
C1 C2
a 10
b 2
I want to join both dataframes such as df_C is
col1 C1 C2
r1 a 10
r1 b 2
r2 aabb 0
r2 b 2
r3 xyz 0
python pandas
marked as duplicate by Sandeep Kadapa, cs95
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Nov 17 '18 at 8:04
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
Thanks, I guess, it might need expert-level knowledge to understand "unsetting" is the same as what I was looking for.
– Watt
Nov 17 '18 at 20:27
add a comment |
This question already has an answer here:
How to unnest (explode) a column in a pandas DataFrame?
6 answers
There are two Pandas DataFrame
df_A = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
col1 col2
r1 [a, b]
r2 [aabb, b]
r3 [xyz]
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
C1 C2
a 10
b 2
I want to join both dataframes such as df_C is
col1 C1 C2
r1 a 10
r1 b 2
r2 aabb 0
r2 b 2
r3 xyz 0
python pandas
This question already has an answer here:
How to unnest (explode) a column in a pandas DataFrame?
6 answers
There are two Pandas DataFrame
df_A = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
col1 col2
r1 [a, b]
r2 [aabb, b]
r3 [xyz]
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
C1 C2
a 10
b 2
I want to join both dataframes such as df_C is
col1 C1 C2
r1 a 10
r1 b 2
r2 aabb 0
r2 b 2
r3 xyz 0
This question already has an answer here:
How to unnest (explode) a column in a pandas DataFrame?
6 answers
python pandas
python pandas
asked Nov 17 '18 at 7:25
WattWatt
1,670104268
1,670104268
marked as duplicate by Sandeep Kadapa, cs95
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Nov 17 '18 at 8:04
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
marked as duplicate by Sandeep Kadapa, cs95
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Nov 17 '18 at 8:04
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
Thanks, I guess, it might need expert-level knowledge to understand "unsetting" is the same as what I was looking for.
– Watt
Nov 17 '18 at 20:27
add a comment |
Thanks, I guess, it might need expert-level knowledge to understand "unsetting" is the same as what I was looking for.
– Watt
Nov 17 '18 at 20:27
Thanks, I guess, it might need expert-level knowledge to understand "unsetting" is the same as what I was looking for.
– Watt
Nov 17 '18 at 20:27
Thanks, I guess, it might need expert-level knowledge to understand "unsetting" is the same as what I was looking for.
– Watt
Nov 17 '18 at 20:27
add a comment |
1 Answer
1
active
oldest
votes
You need:
df = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
df= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()),
'C1':np.concatenate(df.col2.values)})
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
df_B = dict(zip(df_B.C1, df_B.C2))
# {'a': 10, 'b': 2}
df['C2']= df['C1'].apply(lambda x: df_B[x] if x in df_B.keys() else 0)
print(df)
Output:
col1 C1 C2
0 r1 a 10
1 r1 b 2
2 r2 aabb 0
3 r2 b 2
4 r3 xyz 0
Edit
The below code will give you the length of the list in each row.
print(df.col2.str.len())
# 0 2
# 1 2
# 2 1
np.repeat
will repeat the values from col1 based length obtained using above.
eg. r1,r2 will repeat twice.
print(np.repeat(df.col1.values, df.col2.str.len())
# ['r1' 'r1' 'r2' 'r2' 'r3']
Using np.concatenate
on col2.values will result in plain 1D List
print(np.concatenate(df.col2.values))
# ['a' 'b' 'aabb' 'b' 'xyz']
Thanks, can you please explain what you are doing heredf= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()), 'C1':np.concatenate(df.col2.values)})
– Watt
Nov 17 '18 at 20:41
1
@Watt I have edited my answer. Hope it is helpful.
– AkshayNevrekar
Nov 18 '18 at 3:39
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You need:
df = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
df= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()),
'C1':np.concatenate(df.col2.values)})
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
df_B = dict(zip(df_B.C1, df_B.C2))
# {'a': 10, 'b': 2}
df['C2']= df['C1'].apply(lambda x: df_B[x] if x in df_B.keys() else 0)
print(df)
Output:
col1 C1 C2
0 r1 a 10
1 r1 b 2
2 r2 aabb 0
3 r2 b 2
4 r3 xyz 0
Edit
The below code will give you the length of the list in each row.
print(df.col2.str.len())
# 0 2
# 1 2
# 2 1
np.repeat
will repeat the values from col1 based length obtained using above.
eg. r1,r2 will repeat twice.
print(np.repeat(df.col1.values, df.col2.str.len())
# ['r1' 'r1' 'r2' 'r2' 'r3']
Using np.concatenate
on col2.values will result in plain 1D List
print(np.concatenate(df.col2.values))
# ['a' 'b' 'aabb' 'b' 'xyz']
Thanks, can you please explain what you are doing heredf= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()), 'C1':np.concatenate(df.col2.values)})
– Watt
Nov 17 '18 at 20:41
1
@Watt I have edited my answer. Hope it is helpful.
– AkshayNevrekar
Nov 18 '18 at 3:39
add a comment |
You need:
df = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
df= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()),
'C1':np.concatenate(df.col2.values)})
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
df_B = dict(zip(df_B.C1, df_B.C2))
# {'a': 10, 'b': 2}
df['C2']= df['C1'].apply(lambda x: df_B[x] if x in df_B.keys() else 0)
print(df)
Output:
col1 C1 C2
0 r1 a 10
1 r1 b 2
2 r2 aabb 0
3 r2 b 2
4 r3 xyz 0
Edit
The below code will give you the length of the list in each row.
print(df.col2.str.len())
# 0 2
# 1 2
# 2 1
np.repeat
will repeat the values from col1 based length obtained using above.
eg. r1,r2 will repeat twice.
print(np.repeat(df.col1.values, df.col2.str.len())
# ['r1' 'r1' 'r2' 'r2' 'r3']
Using np.concatenate
on col2.values will result in plain 1D List
print(np.concatenate(df.col2.values))
# ['a' 'b' 'aabb' 'b' 'xyz']
Thanks, can you please explain what you are doing heredf= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()), 'C1':np.concatenate(df.col2.values)})
– Watt
Nov 17 '18 at 20:41
1
@Watt I have edited my answer. Hope it is helpful.
– AkshayNevrekar
Nov 18 '18 at 3:39
add a comment |
You need:
df = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
df= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()),
'C1':np.concatenate(df.col2.values)})
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
df_B = dict(zip(df_B.C1, df_B.C2))
# {'a': 10, 'b': 2}
df['C2']= df['C1'].apply(lambda x: df_B[x] if x in df_B.keys() else 0)
print(df)
Output:
col1 C1 C2
0 r1 a 10
1 r1 b 2
2 r2 aabb 0
3 r2 b 2
4 r3 xyz 0
Edit
The below code will give you the length of the list in each row.
print(df.col2.str.len())
# 0 2
# 1 2
# 2 1
np.repeat
will repeat the values from col1 based length obtained using above.
eg. r1,r2 will repeat twice.
print(np.repeat(df.col1.values, df.col2.str.len())
# ['r1' 'r1' 'r2' 'r2' 'r3']
Using np.concatenate
on col2.values will result in plain 1D List
print(np.concatenate(df.col2.values))
# ['a' 'b' 'aabb' 'b' 'xyz']
You need:
df = pd.DataFrame([['r1', ['a','b']], ['r2',['aabb','b']], ['r3', ['xyz']]], columns=['col1', 'col2'])
df= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()),
'C1':np.concatenate(df.col2.values)})
df_B = pd.DataFrame([['a', 10], ['b',2]], columns=['C1', 'C2'])
df_B = dict(zip(df_B.C1, df_B.C2))
# {'a': 10, 'b': 2}
df['C2']= df['C1'].apply(lambda x: df_B[x] if x in df_B.keys() else 0)
print(df)
Output:
col1 C1 C2
0 r1 a 10
1 r1 b 2
2 r2 aabb 0
3 r2 b 2
4 r3 xyz 0
Edit
The below code will give you the length of the list in each row.
print(df.col2.str.len())
# 0 2
# 1 2
# 2 1
np.repeat
will repeat the values from col1 based length obtained using above.
eg. r1,r2 will repeat twice.
print(np.repeat(df.col1.values, df.col2.str.len())
# ['r1' 'r1' 'r2' 'r2' 'r3']
Using np.concatenate
on col2.values will result in plain 1D List
print(np.concatenate(df.col2.values))
# ['a' 'b' 'aabb' 'b' 'xyz']
edited Nov 18 '18 at 3:39
answered Nov 17 '18 at 7:57
AkshayNevrekarAkshayNevrekar
6,261102143
6,261102143
Thanks, can you please explain what you are doing heredf= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()), 'C1':np.concatenate(df.col2.values)})
– Watt
Nov 17 '18 at 20:41
1
@Watt I have edited my answer. Hope it is helpful.
– AkshayNevrekar
Nov 18 '18 at 3:39
add a comment |
Thanks, can you please explain what you are doing heredf= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()), 'C1':np.concatenate(df.col2.values)})
– Watt
Nov 17 '18 at 20:41
1
@Watt I have edited my answer. Hope it is helpful.
– AkshayNevrekar
Nov 18 '18 at 3:39
Thanks, can you please explain what you are doing here
df= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()), 'C1':np.concatenate(df.col2.values)})
– Watt
Nov 17 '18 at 20:41
Thanks, can you please explain what you are doing here
df= pd.DataFrame({'col1':np.repeat(df.col1.values, df.col2.str.len()), 'C1':np.concatenate(df.col2.values)})
– Watt
Nov 17 '18 at 20:41
1
1
@Watt I have edited my answer. Hope it is helpful.
– AkshayNevrekar
Nov 18 '18 at 3:39
@Watt I have edited my answer. Hope it is helpful.
– AkshayNevrekar
Nov 18 '18 at 3:39
add a comment |
Thanks, I guess, it might need expert-level knowledge to understand "unsetting" is the same as what I was looking for.
– Watt
Nov 17 '18 at 20:27