Pandas groupby apply anomaly with datetime
While experimenting Pandas in Jupyter, I noticed very strange symptom. I reduce it down to a bare minimum code that demonstrates the symptom:
import pandas as pd
import numpy as np
from datetime import datetime
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A B
0 a 2018-11-01
1 b 2018-11-02
2 c 2018-11-03
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 1541376000000000000
1 b 1541376000000000000
2 c 1541376000000000000
df2['C']
0 1541376000000000000
1 1541376000000000000
2 1541376000000000000
Name: C, dtype: int64
As you can see, the C column ended up being int64 type instead of the expected datetime64[ns] type. But if I don't have the B column then C column correctly ends up being datetime64[ns].
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
# 'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A
0 a
1 b
2 c
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 2018-11-05
1 b 2018-11-05
2 c 2018-11-05
df2['C']
0 2018-11-05
1 2018-11-05
2 2018-11-05
Name: C, dtype: datetime64[ns]
I have no clue what is happening. Anyone any idea? I'm using Python 3.6 and Pandas 0.23.1
python pandas datetime group-by
asked Nov 20 '18 at 6:40
JakeJake
7182616
add a comment |
While experimenting Pandas in Jupyter, I noticed very strange symptom. I reduce it down to a bare minimum code that demonstrates the symptom:
import pandas as pd
import numpy as np
from datetime import datetime
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A B
0 a 2018-11-01
1 b 2018-11-02
2 c 2018-11-03
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 1541376000000000000
1 b 1541376000000000000
2 c 1541376000000000000
df2['C']
0 1541376000000000000
1 1541376000000000000
2 1541376000000000000
Name: C, dtype: int64
As you can see, the C column ended up being int64 type instead of the expected datetime64[ns] type. But if I don't have the B column then C column correctly ends up being datetime64[ns].
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
# 'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A
0 a
1 b
2 c
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 2018-11-05
1 b 2018-11-05
2 c 2018-11-05
df2['C']
0 2018-11-05
1 2018-11-05
2 2018-11-05
Name: C, dtype: datetime64[ns]
I have no clue what is happening. Anyone any idea? I'm using Python 3.6 and Pandas 0.23.1
python pandas datetime group-by
asked Nov 20 '18 at 6:40
JakeJake
7182616
I am using Python 2.7 and I cannot reproduce it. Also the first output isdatetime
– Joe
Nov 20 '18 at 6:48
add a comment |
While experimenting Pandas in Jupyter, I noticed very strange symptom. I reduce it down to a bare minimum code that demonstrates the symptom:
import pandas as pd
import numpy as np
from datetime import datetime
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A B
0 a 2018-11-01
1 b 2018-11-02
2 c 2018-11-03
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 1541376000000000000
1 b 1541376000000000000
2 c 1541376000000000000
df2['C']
0 1541376000000000000
1 1541376000000000000
2 1541376000000000000
Name: C, dtype: int64
As you can see, the C column ended up being int64 type instead of the expected datetime64[ns] type. But if I don't have the B column then C column correctly ends up being datetime64[ns].
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
# 'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A
0 a
1 b
2 c
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 2018-11-05
1 b 2018-11-05
2 c 2018-11-05
df2['C']
0 2018-11-05
1 2018-11-05
2 2018-11-05
Name: C, dtype: datetime64[ns]
I have no clue what is happening. Anyone any idea? I'm using Python 3.6 and Pandas 0.23.1
python pandas datetime group-by
asked Nov 20 '18 at 6:40
JakeJake
7182616
While experimenting Pandas in Jupyter, I noticed very strange symptom. I reduce it down to a bare minimum code that demonstrates the symptom:
import pandas as pd
import numpy as np
from datetime import datetime
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A B
0 a 2018-11-01
1 b 2018-11-02
2 c 2018-11-03
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 1541376000000000000
1 b 1541376000000000000
2 c 1541376000000000000
df2['C']
0 1541376000000000000
1 1541376000000000000
2 1541376000000000000
Name: C, dtype: int64
As you can see, the C column ended up being int64 type instead of the expected datetime64[ns] type. But if I don't have the B column then C column correctly ends up being datetime64[ns].
df = pd.DataFrame({
'A': ['a', 'b', 'c'],
# 'B': [datetime(2018, 11, 1), datetime(2018, 11, 2), datetime(2018, 11, 3) ]
})
df
A
0 a
1 b
2 c
def process(gdf):
return pd.Series({
'C': datetime(2018, 11, 5)
})
df2 = df.groupby(['A']).apply(process).reset_index()
df2
A C
0 a 2018-11-05
1 b 2018-11-05
2 c 2018-11-05
df2['C']
0 2018-11-05
1 2018-11-05
2 2018-11-05
Name: C, dtype: datetime64[ns]
I have no clue what is happening. Anyone any idea? I'm using Python 3.6 and Pandas 0.23.1
python pandas datetime group-by
python pandas datetime group-by
asked Nov 20 '18 at 6:40
JakeJake
7182616
asked Nov 20 '18 at 6:40
JakeJake
7182616
asked Nov 20 '18 at 6:40
JakeJake
7182616
asked Nov 20 '18 at 6:40
JakeJake
7182616
asked Nov 20 '18 at 6:40
JakeJake
7182616
7182616
I am using Python 2.7 and I cannot reproduce it. Also the first output isdatetime
– Joe
Nov 20 '18 at 6:48
add a comment |
I am using Python 2.7 and I cannot reproduce it. Also the first output isdatetime
– Joe
Nov 20 '18 at 6:48
I am using Python 2.7 and I cannot reproduce it. Also the first output is
datetime– Joe
Nov 20 '18 at 6:48
I am using Python 2.7 and I cannot reproduce it. Also the first output is
datetime– Joe
Nov 20 '18 at 6:48
add a comment |
1 Answer
1
active
oldest
votes
First it seems bug.
In my opinion here is possible create new column for each group and return not Series, but gdp group:
def process(gdf):
gdf['C'] = datetime(2018, 11, 5)
return gdf
df2 = df.groupby(['A']).apply(process)
print (df2)
A B C
0 a 2018-11-01 2018-11-05
1 b 2018-11-02 2018-11-05
2 c 2018-11-03 2018-11-05
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
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
First it seems bug.
In my opinion here is possible create new column for each group and return not Series, but gdp group:
def process(gdf):
gdf['C'] = datetime(2018, 11, 5)
return gdf
df2 = df.groupby(['A']).apply(process)
print (df2)
A B C
0 a 2018-11-01 2018-11-05
1 b 2018-11-02 2018-11-05
2 c 2018-11-03 2018-11-05
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
add a comment |
First it seems bug.
In my opinion here is possible create new column for each group and return not Series, but gdp group:
def process(gdf):
gdf['C'] = datetime(2018, 11, 5)
return gdf
df2 = df.groupby(['A']).apply(process)
print (df2)
A B C
0 a 2018-11-01 2018-11-05
1 b 2018-11-02 2018-11-05
2 c 2018-11-03 2018-11-05
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
add a comment |
First it seems bug.
In my opinion here is possible create new column for each group and return not Series, but gdp group:
def process(gdf):
gdf['C'] = datetime(2018, 11, 5)
return gdf
df2 = df.groupby(['A']).apply(process)
print (df2)
A B C
0 a 2018-11-01 2018-11-05
1 b 2018-11-02 2018-11-05
2 c 2018-11-03 2018-11-05
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
First it seems bug.
In my opinion here is possible create new column for each group and return not Series, but gdp group:
def process(gdf):
gdf['C'] = datetime(2018, 11, 5)
return gdf
df2 = df.groupby(['A']).apply(process)
print (df2)
A B C
0 a 2018-11-01 2018-11-05
1 b 2018-11-02 2018-11-05
2 c 2018-11-03 2018-11-05
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
answered Nov 20 '18 at 6:50
jezraeljezrael
338k25288361
338k25288361
add a comment |
add a comment |
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I am using Python 2.7 and I cannot reproduce it. Also the first output is
datetime– Joe
Nov 20 '18 at 6:48