Reshape pandas dataframe to turn categorical columns into individual columns
2
I have data that looks like this:
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 85 14
1 B Natural Gas Salt water Gas Water 95 78
2 C Gasoline Waste water Refined Water 33 25
3 D Diesel Motor oil Refined Oil 49 54
4 E Bitumen Sour Gas Oil Gas 92 86
The Category and Quantity columns refer to the corresponding the Substance columns.
I want to expand the Category columns as a new column for each unique value and have the Quantity value as the cell value. Non-existant categories would be NaN. So the resulting frame would look like this:
ID Oil Gas Water Refined
0 A 85 14 NaN NaN
1 B NaN 95 78 NaN
2 C NaN NaN 25 33
3 D 54 NaN NaN 49
4 E 92 86 NaN NaN
I tried .melt() followed by .pivot_table() but for some reason values get duplicated across the new category columns.
python pandas
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
add a comment |
2
I have data that looks like this:
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 85 14
1 B Natural Gas Salt water Gas Water 95 78
2 C Gasoline Waste water Refined Water 33 25
3 D Diesel Motor oil Refined Oil 49 54
4 E Bitumen Sour Gas Oil Gas 92 86
The Category and Quantity columns refer to the corresponding the Substance columns.
I want to expand the Category columns as a new column for each unique value and have the Quantity value as the cell value. Non-existant categories would be NaN. So the resulting frame would look like this:
ID Oil Gas Water Refined
0 A 85 14 NaN NaN
1 B NaN 95 78 NaN
2 C NaN NaN 25 33
3 D 54 NaN NaN 49
4 E 92 86 NaN NaN
I tried .melt() followed by .pivot_table() but for some reason values get duplicated across the new category columns.
python pandas
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
add a comment |
2
2
2
I have data that looks like this:
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 85 14
1 B Natural Gas Salt water Gas Water 95 78
2 C Gasoline Waste water Refined Water 33 25
3 D Diesel Motor oil Refined Oil 49 54
4 E Bitumen Sour Gas Oil Gas 92 86
The Category and Quantity columns refer to the corresponding the Substance columns.
I want to expand the Category columns as a new column for each unique value and have the Quantity value as the cell value. Non-existant categories would be NaN. So the resulting frame would look like this:
ID Oil Gas Water Refined
0 A 85 14 NaN NaN
1 B NaN 95 78 NaN
2 C NaN NaN 25 33
3 D 54 NaN NaN 49
4 E 92 86 NaN NaN
I tried .melt() followed by .pivot_table() but for some reason values get duplicated across the new category columns.
python pandas
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
I have data that looks like this:
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 85 14
1 B Natural Gas Salt water Gas Water 95 78
2 C Gasoline Waste water Refined Water 33 25
3 D Diesel Motor oil Refined Oil 49 54
4 E Bitumen Sour Gas Oil Gas 92 86
The Category and Quantity columns refer to the corresponding the Substance columns.
I want to expand the Category columns as a new column for each unique value and have the Quantity value as the cell value. Non-existant categories would be NaN. So the resulting frame would look like this:
ID Oil Gas Water Refined
0 A 85 14 NaN NaN
1 B NaN 95 78 NaN
2 C NaN NaN 25 33
3 D 54 NaN NaN 49
4 E 92 86 NaN NaN
I tried .melt() followed by .pivot_table() but for some reason values get duplicated across the new category columns.
python pandas
python pandas
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
asked Nov 14 '18 at 21:56
robrocrobroc
4851313
4851313
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
2
You need to use pd.melt then groupby:
np.random.seed(0)
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+')
.groupby(['ID','Category'])['Quantity'].sum()
.unstack().reset_index()
Output:
Category ID Gas Oil Refined Water
0 A 19.0 54.0 NaN NaN
1 B 57.0 NaN NaN 93.0
2 C NaN NaN 74.0 31.0
3 D NaN 46.0 77.0 NaN
4 E 97.0 77.0 NaN NaN
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
1
This works, but not as is. It was creating a ton of duplicate columns and adding all of the numbers, resulting in inaccurate values. But adding two methods to the chain,reset_indexanddrop_duplicates, worked:pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+') .reset_index().drop_duplicates(subset=['ID', 'Num']) .groupby(['ID','Category'])['Quantity'].sum() .unstack().reset_index()
– robroc
Nov 14 '18 at 22:48
I'll add this nifty solution also needs some adaptation for the olderpandas0.19.
– kabanus
Nov 14 '18 at 22:53
add a comment |
0
Here is my semi-manual approach:
>>> df
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 74 49
1 B Natural Gas Salt water Gas Water 75 91
2 C Gasoline Waste water Refined Water 24 38
3 D Diesel Motor oil Refined Oil 19 95
4 E Bitumen Sour Gas Oil Gas 50 35
>>> newdf=pd.DataFrame(columns=set(df[['Category1','Category2']].values.flatten()),index=df.index)
>>> for name in newdf:
newdf[name]=pd.concat([df[df['Category1']==name]['Quantity1'],df[df['Category2']==name]['Quantity2']])
...
>>> newdf
Gas Oil Water Refined
0 49 74 NaN NaN
1 75 NaN 91 NaN
2 NaN NaN 38 24
3 NaN 95 NaN 19
4 35 50 NaN NaN
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
You need to use pd.melt then groupby:
np.random.seed(0)
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+')
.groupby(['ID','Category'])['Quantity'].sum()
.unstack().reset_index()
Output:
Category ID Gas Oil Refined Water
0 A 19.0 54.0 NaN NaN
1 B 57.0 NaN NaN 93.0
2 C NaN NaN 74.0 31.0
3 D NaN 46.0 77.0 NaN
4 E 97.0 77.0 NaN NaN
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
1
This works, but not as is. It was creating a ton of duplicate columns and adding all of the numbers, resulting in inaccurate values. But adding two methods to the chain,reset_indexanddrop_duplicates, worked:pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+') .reset_index().drop_duplicates(subset=['ID', 'Num']) .groupby(['ID','Category'])['Quantity'].sum() .unstack().reset_index()
– robroc
Nov 14 '18 at 22:48
I'll add this nifty solution also needs some adaptation for the olderpandas0.19.
– kabanus
Nov 14 '18 at 22:53
add a comment |
2
You need to use pd.melt then groupby:
np.random.seed(0)
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+')
.groupby(['ID','Category'])['Quantity'].sum()
.unstack().reset_index()
Output:
Category ID Gas Oil Refined Water
0 A 19.0 54.0 NaN NaN
1 B 57.0 NaN NaN 93.0
2 C NaN NaN 74.0 31.0
3 D NaN 46.0 77.0 NaN
4 E 97.0 77.0 NaN NaN
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
1
This works, but not as is. It was creating a ton of duplicate columns and adding all of the numbers, resulting in inaccurate values. But adding two methods to the chain,reset_indexanddrop_duplicates, worked:pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+') .reset_index().drop_duplicates(subset=['ID', 'Num']) .groupby(['ID','Category'])['Quantity'].sum() .unstack().reset_index()
– robroc
Nov 14 '18 at 22:48
I'll add this nifty solution also needs some adaptation for the olderpandas0.19.
– kabanus
Nov 14 '18 at 22:53
add a comment |
2
2
2
You need to use pd.melt then groupby:
np.random.seed(0)
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+')
.groupby(['ID','Category'])['Quantity'].sum()
.unstack().reset_index()
Output:
Category ID Gas Oil Refined Water
0 A 19.0 54.0 NaN NaN
1 B 57.0 NaN NaN 93.0
2 C NaN NaN 74.0 31.0
3 D NaN 46.0 77.0 NaN
4 E 97.0 77.0 NaN NaN
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
You need to use pd.melt then groupby:
np.random.seed(0)
df = pd.DataFrame(data=[list('ABCDE'),
['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
['Gas', 'Water', 'Water', 'Oil', 'Gas'],
list(np.random.randint(10, 100, 5)),
list(np.random.randint(10, 100, 5))]
).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']
pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+')
.groupby(['ID','Category'])['Quantity'].sum()
.unstack().reset_index()
Output:
Category ID Gas Oil Refined Water
0 A 19.0 54.0 NaN NaN
1 B 57.0 NaN NaN 93.0
2 C NaN NaN 74.0 31.0
3 D NaN 46.0 77.0 NaN
4 E 97.0 77.0 NaN NaN
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
answered Nov 14 '18 at 22:05
Scott BostonScott Boston
55.7k73156
55.7k73156
1
This works, but not as is. It was creating a ton of duplicate columns and adding all of the numbers, resulting in inaccurate values. But adding two methods to the chain,reset_indexanddrop_duplicates, worked:pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+') .reset_index().drop_duplicates(subset=['ID', 'Num']) .groupby(['ID','Category'])['Quantity'].sum() .unstack().reset_index()
– robroc
Nov 14 '18 at 22:48
I'll add this nifty solution also needs some adaptation for the olderpandas0.19.
– kabanus
Nov 14 '18 at 22:53
add a comment |
1
This works, but not as is. It was creating a ton of duplicate columns and adding all of the numbers, resulting in inaccurate values. But adding two methods to the chain,reset_indexanddrop_duplicates, worked:pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+') .reset_index().drop_duplicates(subset=['ID', 'Num']) .groupby(['ID','Category'])['Quantity'].sum() .unstack().reset_index()
– robroc
Nov 14 '18 at 22:48
I'll add this nifty solution also needs some adaptation for the olderpandas0.19.
– kabanus
Nov 14 '18 at 22:53
1
1
This works, but not as is. It was creating a ton of duplicate columns and adding all of the numbers, resulting in inaccurate values. But adding two methods to the chain,
reset_index and drop_duplicates, worked: pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+') .reset_index().drop_duplicates(subset=['ID', 'Num']) .groupby(['ID','Category'])['Quantity'].sum() .unstack().reset_index()– robroc
Nov 14 '18 at 22:48
This works, but not as is. It was creating a ton of duplicate columns and adding all of the numbers, resulting in inaccurate values. But adding two methods to the chain,
reset_index and drop_duplicates, worked: pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+') .reset_index().drop_duplicates(subset=['ID', 'Num']) .groupby(['ID','Category'])['Quantity'].sum() .unstack().reset_index()– robroc
Nov 14 '18 at 22:48
I'll add this nifty solution also needs some adaptation for the older
pandas 0.19.– kabanus
Nov 14 '18 at 22:53
I'll add this nifty solution also needs some adaptation for the older
pandas 0.19.– kabanus
Nov 14 '18 at 22:53
add a comment |
0
Here is my semi-manual approach:
>>> df
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 74 49
1 B Natural Gas Salt water Gas Water 75 91
2 C Gasoline Waste water Refined Water 24 38
3 D Diesel Motor oil Refined Oil 19 95
4 E Bitumen Sour Gas Oil Gas 50 35
>>> newdf=pd.DataFrame(columns=set(df[['Category1','Category2']].values.flatten()),index=df.index)
>>> for name in newdf:
newdf[name]=pd.concat([df[df['Category1']==name]['Quantity1'],df[df['Category2']==name]['Quantity2']])
...
>>> newdf
Gas Oil Water Refined
0 49 74 NaN NaN
1 75 NaN 91 NaN
2 NaN NaN 38 24
3 NaN 95 NaN 19
4 35 50 NaN NaN
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
add a comment |
0
Here is my semi-manual approach:
>>> df
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 74 49
1 B Natural Gas Salt water Gas Water 75 91
2 C Gasoline Waste water Refined Water 24 38
3 D Diesel Motor oil Refined Oil 19 95
4 E Bitumen Sour Gas Oil Gas 50 35
>>> newdf=pd.DataFrame(columns=set(df[['Category1','Category2']].values.flatten()),index=df.index)
>>> for name in newdf:
newdf[name]=pd.concat([df[df['Category1']==name]['Quantity1'],df[df['Category2']==name]['Quantity2']])
...
>>> newdf
Gas Oil Water Refined
0 49 74 NaN NaN
1 75 NaN 91 NaN
2 NaN NaN 38 24
3 NaN 95 NaN 19
4 35 50 NaN NaN
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
add a comment |
0
0
0
Here is my semi-manual approach:
>>> df
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 74 49
1 B Natural Gas Salt water Gas Water 75 91
2 C Gasoline Waste water Refined Water 24 38
3 D Diesel Motor oil Refined Oil 19 95
4 E Bitumen Sour Gas Oil Gas 50 35
>>> newdf=pd.DataFrame(columns=set(df[['Category1','Category2']].values.flatten()),index=df.index)
>>> for name in newdf:
newdf[name]=pd.concat([df[df['Category1']==name]['Quantity1'],df[df['Category2']==name]['Quantity2']])
...
>>> newdf
Gas Oil Water Refined
0 49 74 NaN NaN
1 75 NaN 91 NaN
2 NaN NaN 38 24
3 NaN 95 NaN 19
4 35 50 NaN NaN
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
Here is my semi-manual approach:
>>> df
ID Substance1 Substance2 Category1 Category2 Quantity1 Quantity2
0 A Crude Oil Natural Gas Oil Gas 74 49
1 B Natural Gas Salt water Gas Water 75 91
2 C Gasoline Waste water Refined Water 24 38
3 D Diesel Motor oil Refined Oil 19 95
4 E Bitumen Sour Gas Oil Gas 50 35
>>> newdf=pd.DataFrame(columns=set(df[['Category1','Category2']].values.flatten()),index=df.index)
>>> for name in newdf:
newdf[name]=pd.concat([df[df['Category1']==name]['Quantity1'],df[df['Category2']==name]['Quantity2']])
...
>>> newdf
Gas Oil Water Refined
0 49 74 NaN NaN
1 75 NaN 91 NaN
2 NaN NaN 38 24
3 NaN 95 NaN 19
4 35 50 NaN NaN
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
answered Nov 14 '18 at 22:39
kabanuskabanus
11.8k31439
11.8k31439
add a comment |
add a comment |
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