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Pandas - Create a symmetric matrix that counts the number of records
The Next CEO of Stack OverflowCount the number occurrences of a character in a stringPandas: create two new columns in a dataframe with values calculated from a pre-existing columnHow can I replace all the NaN values with Zero's in a column of a pandas dataframeCreating an empty Pandas DataFrame, then filling it?“Large data” work flows using pandasChange data type of columns in PandasHow do I get the row count of a Pandas dataframe?How to make good reproducible pandas examplesPandas Replace NaN with blank/empty stringPopulating a “count matrix” with combinations of pandas DataFrame rows
I have a dataframe that looks like the one below
ID | Value
1 | A
1 | B
1 | C
2 | B
2 | C
I want to create a symmetric matrix of based on Value:
A B C
A 1 1 1
B 1 2 2
C 1 2 2
This basically indicates how many people have both values (v1,v2). I am currently using for loops to scan the dataframe for every combination but was wondering if there was an easier way to do it using pandas.
python pandas
asked Mar 8 at 14:26
NiviNivi
4101513
add a comment |
I have a dataframe that looks like the one below
ID | Value
1 | A
1 | B
1 | C
2 | B
2 | C
I want to create a symmetric matrix of based on Value:
A B C
A 1 1 1
B 1 2 2
C 1 2 2
This basically indicates how many people have both values (v1,v2). I am currently using for loops to scan the dataframe for every combination but was wondering if there was an easier way to do it using pandas.
python pandas
asked Mar 8 at 14:26
NiviNivi
4101513
add a comment |
I have a dataframe that looks like the one below
ID | Value
1 | A
1 | B
1 | C
2 | B
2 | C
I want to create a symmetric matrix of based on Value:
A B C
A 1 1 1
B 1 2 2
C 1 2 2
This basically indicates how many people have both values (v1,v2). I am currently using for loops to scan the dataframe for every combination but was wondering if there was an easier way to do it using pandas.
python pandas
asked Mar 8 at 14:26
NiviNivi
4101513
I have a dataframe that looks like the one below
ID | Value
1 | A
1 | B
1 | C
2 | B
2 | C
I want to create a symmetric matrix of based on Value:
A B C
A 1 1 1
B 1 2 2
C 1 2 2
This basically indicates how many people have both values (v1,v2). I am currently using for loops to scan the dataframe for every combination but was wondering if there was an easier way to do it using pandas.
python pandas
python pandas
asked Mar 8 at 14:26
NiviNivi
4101513
asked Mar 8 at 14:26
NiviNivi
4101513
asked Mar 8 at 14:26
NiviNivi
4101513
asked Mar 8 at 14:26
NiviNivi
4101513
asked Mar 8 at 14:26
NiviNivi
4101513
4101513
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Use merge with cross join by ID column with crosstab and DataFrame.rename_axis for remove index and columns names:
df = pd.merge(df, df, on='ID')
df = pd.crosstab(df['Value_x'], df['Value_y']).rename_axis(None).rename_axis(None, axis=1)
print (df)
A B C
A 1 1 1
B 1 2 2
C 1 2 2
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
1
'Value_x' and 'Value_y' are to be specified or are they inbuilt keywords? Thanks for the help.
– Nivi
Mar 8 at 14:30
1
Got it. Thanks!
– Nivi
Mar 8 at 14:46
1
@Nivi - It isindexandcolumnsnames created from columns names aftercrosstab
– jezrael
Mar 8 at 14:46
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
Use merge with cross join by ID column with crosstab and DataFrame.rename_axis for remove index and columns names:
df = pd.merge(df, df, on='ID')
df = pd.crosstab(df['Value_x'], df['Value_y']).rename_axis(None).rename_axis(None, axis=1)
print (df)
A B C
A 1 1 1
B 1 2 2
C 1 2 2
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
1
'Value_x' and 'Value_y' are to be specified or are they inbuilt keywords? Thanks for the help.
– Nivi
Mar 8 at 14:30
1
Got it. Thanks!
– Nivi
Mar 8 at 14:46
1
@Nivi - It isindexandcolumnsnames created from columns names aftercrosstab
– jezrael
Mar 8 at 14:46
add a comment |
Use merge with cross join by ID column with crosstab and DataFrame.rename_axis for remove index and columns names:
df = pd.merge(df, df, on='ID')
df = pd.crosstab(df['Value_x'], df['Value_y']).rename_axis(None).rename_axis(None, axis=1)
print (df)
A B C
A 1 1 1
B 1 2 2
C 1 2 2
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
1
'Value_x' and 'Value_y' are to be specified or are they inbuilt keywords? Thanks for the help.
– Nivi
Mar 8 at 14:30
1
Got it. Thanks!
– Nivi
Mar 8 at 14:46
1
@Nivi - It isindexandcolumnsnames created from columns names aftercrosstab
– jezrael
Mar 8 at 14:46
add a comment |
Use merge with cross join by ID column with crosstab and DataFrame.rename_axis for remove index and columns names:
df = pd.merge(df, df, on='ID')
df = pd.crosstab(df['Value_x'], df['Value_y']).rename_axis(None).rename_axis(None, axis=1)
print (df)
A B C
A 1 1 1
B 1 2 2
C 1 2 2
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
Use merge with cross join by ID column with crosstab and DataFrame.rename_axis for remove index and columns names:
df = pd.merge(df, df, on='ID')
df = pd.crosstab(df['Value_x'], df['Value_y']).rename_axis(None).rename_axis(None, axis=1)
print (df)
A B C
A 1 1 1
B 1 2 2
C 1 2 2
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
edited Mar 8 at 14:30
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
answered Mar 8 at 14:28
jezraeljezrael
352k26317391
352k26317391
1
'Value_x' and 'Value_y' are to be specified or are they inbuilt keywords? Thanks for the help.
– Nivi
Mar 8 at 14:30
1
Got it. Thanks!
– Nivi
Mar 8 at 14:46
1
@Nivi - It isindexandcolumnsnames created from columns names aftercrosstab
– jezrael
Mar 8 at 14:46
add a comment |
1
'Value_x' and 'Value_y' are to be specified or are they inbuilt keywords? Thanks for the help.
– Nivi
Mar 8 at 14:30
1
Got it. Thanks!
– Nivi
Mar 8 at 14:46
1
@Nivi - It isindexandcolumnsnames created from columns names aftercrosstab
– jezrael
Mar 8 at 14:46
1
1
'Value_x' and 'Value_y' are to be specified or are they inbuilt keywords? Thanks for the help.
– Nivi
Mar 8 at 14:30
'Value_x' and 'Value_y' are to be specified or are they inbuilt keywords? Thanks for the help.
– Nivi
Mar 8 at 14:30
1
1
Got it. Thanks!
– Nivi
Mar 8 at 14:46
Got it. Thanks!
– Nivi
Mar 8 at 14:46
1
1
@Nivi - It is
index and columns names created from columns names after crosstab– jezrael
Mar 8 at 14:46
@Nivi - It is
index and columns names created from columns names after crosstab– jezrael
Mar 8 at 14:46
add a comment |
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