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Nan in pd.DataFrame (simmetrical matrix)
How can I check for NaN values?How to drop rows of Pandas DataFrame whose value in certain columns is NaNHow to split a column into two columns?Creating a pandas DataFrame from columns of other DataFrames with similar indexesExtracting just Month and Year separately from Pandas Datetime columnNaN values when new column added to pandas DataFrameSum across all NaNs in pandas returns zero?Replace cell value in pandas dataframe where value is 'NaN' with value from another/same dataframeHow to pivot a dataframeDouble loop to pd.DataFrame
I've got a dataframe like this one. I'd like to remove the nans and shift up the cells. Then add a date column and set it as index.
ciao google microsoft
Search Volume 368000 NaN NaN
Search Volume 368000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN NaN 135000
Search Volume NaN NaN 135000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
The output should be like:
date = ['20140115', '20140215', '20140315', '20140415', '20140515', '20140615']
date ciao google microsoft
20140115 368000 37200000 135000
20140215 368000 37200000 135000
20140315 450000 37200000 110000
20140415 450000 37200000 110000
20140515 450000 37200000 110000
20140615 450000 37200000 110000
Looks simple but I don't know how to do it. Thanks
python pandas
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
add a comment |
I've got a dataframe like this one. I'd like to remove the nans and shift up the cells. Then add a date column and set it as index.
ciao google microsoft
Search Volume 368000 NaN NaN
Search Volume 368000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN NaN 135000
Search Volume NaN NaN 135000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
The output should be like:
date = ['20140115', '20140215', '20140315', '20140415', '20140515', '20140615']
date ciao google microsoft
20140115 368000 37200000 135000
20140215 368000 37200000 135000
20140315 450000 37200000 110000
20140415 450000 37200000 110000
20140515 450000 37200000 110000
20140615 450000 37200000 110000
Looks simple but I don't know how to do it. Thanks
python pandas
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
add a comment |
I've got a dataframe like this one. I'd like to remove the nans and shift up the cells. Then add a date column and set it as index.
ciao google microsoft
Search Volume 368000 NaN NaN
Search Volume 368000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN NaN 135000
Search Volume NaN NaN 135000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
The output should be like:
date = ['20140115', '20140215', '20140315', '20140415', '20140515', '20140615']
date ciao google microsoft
20140115 368000 37200000 135000
20140215 368000 37200000 135000
20140315 450000 37200000 110000
20140415 450000 37200000 110000
20140515 450000 37200000 110000
20140615 450000 37200000 110000
Looks simple but I don't know how to do it. Thanks
python pandas
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
I've got a dataframe like this one. I'd like to remove the nans and shift up the cells. Then add a date column and set it as index.
ciao google microsoft
Search Volume 368000 NaN NaN
Search Volume 368000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume 450000 NaN NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN 37200000 NaN
Search Volume NaN NaN 135000
Search Volume NaN NaN 135000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
Search Volume NaN NaN 110000
The output should be like:
date = ['20140115', '20140215', '20140315', '20140415', '20140515', '20140615']
date ciao google microsoft
20140115 368000 37200000 135000
20140215 368000 37200000 135000
20140315 450000 37200000 110000
20140415 450000 37200000 110000
20140515 450000 37200000 110000
20140615 450000 37200000 110000
Looks simple but I don't know how to do it. Thanks
python pandas
python pandas
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
edited Mar 25 at 16:44
wpercy
6,9064 gold badges24 silver badges35 bronze badges
6,9064 gold badges24 silver badges35 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
asked Mar 25 at 16:42
SkuPakSkuPak
103 bronze badges
103 bronze badges
add a comment |
add a comment |
5 Answers
5
active
oldest
votes
you could use apply with dropna:
df = df.apply(lambda x: pd.Series(x.dropna().values)).fillna('')
df['date'] = date
print(df)
output:
ciao google microsoft date
368000.0 37200000.0 135000.0 20140115
368000.0 37200000.0 135000.0 20140215
450000.0 37200000.0 110000.0 20140315
450000.0 37200000.0 110000.0 20140415
450000.0 37200000.0 110000.0 20140515
450000.0 37200000.0 110000.0 20140615
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
add a comment |
You can also use dropna on the columns as series
df1=pd.DataFrame(data=[df[i].dropna().values for i in df.columns]).T
df1.index=dates
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
add a comment |
One tricky solution cause by you have duplicate index
pd.concat([df[x].dropna() for x in df.columns],1)
Out[24]:
ciao google microsoft
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
add a comment |
My proposition is:
pd.DataFrame(data= colName: df[colName].dropna().values for colName in df.columns ,
index=['20140115', '20140215', '20140315', '20140415', '20140515', '20140615'])
The main point is a dictionary comprehension, executed for each column.
dropna removes NaN items and values allows to free oneself from
index values.
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
add a comment |
This should work:
denulled = col: df.loc[df[col].notnull(),col].values for col in df.columns
df_out = pd.DataFrame(denulled, index=date)
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
add a comment |
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5 Answers
5
active
oldest
votes
5 Answers
5
active
oldest
votes
active
oldest
votes
active
oldest
votes
you could use apply with dropna:
df = df.apply(lambda x: pd.Series(x.dropna().values)).fillna('')
df['date'] = date
print(df)
output:
ciao google microsoft date
368000.0 37200000.0 135000.0 20140115
368000.0 37200000.0 135000.0 20140215
450000.0 37200000.0 110000.0 20140315
450000.0 37200000.0 110000.0 20140415
450000.0 37200000.0 110000.0 20140515
450000.0 37200000.0 110000.0 20140615
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
add a comment |
you could use apply with dropna:
df = df.apply(lambda x: pd.Series(x.dropna().values)).fillna('')
df['date'] = date
print(df)
output:
ciao google microsoft date
368000.0 37200000.0 135000.0 20140115
368000.0 37200000.0 135000.0 20140215
450000.0 37200000.0 110000.0 20140315
450000.0 37200000.0 110000.0 20140415
450000.0 37200000.0 110000.0 20140515
450000.0 37200000.0 110000.0 20140615
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
add a comment |
you could use apply with dropna:
df = df.apply(lambda x: pd.Series(x.dropna().values)).fillna('')
df['date'] = date
print(df)
output:
ciao google microsoft date
368000.0 37200000.0 135000.0 20140115
368000.0 37200000.0 135000.0 20140215
450000.0 37200000.0 110000.0 20140315
450000.0 37200000.0 110000.0 20140415
450000.0 37200000.0 110000.0 20140515
450000.0 37200000.0 110000.0 20140615
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
you could use apply with dropna:
df = df.apply(lambda x: pd.Series(x.dropna().values)).fillna('')
df['date'] = date
print(df)
output:
ciao google microsoft date
368000.0 37200000.0 135000.0 20140115
368000.0 37200000.0 135000.0 20140215
450000.0 37200000.0 110000.0 20140315
450000.0 37200000.0 110000.0 20140415
450000.0 37200000.0 110000.0 20140515
450000.0 37200000.0 110000.0 20140615
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
answered Mar 25 at 17:06
FrenchyFrenchy
2,6662 gold badges5 silver badges18 bronze badges
2,6662 gold badges5 silver badges18 bronze badges
add a comment |
add a comment |
You can also use dropna on the columns as series
df1=pd.DataFrame(data=[df[i].dropna().values for i in df.columns]).T
df1.index=dates
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
add a comment |
You can also use dropna on the columns as series
df1=pd.DataFrame(data=[df[i].dropna().values for i in df.columns]).T
df1.index=dates
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
add a comment |
You can also use dropna on the columns as series
df1=pd.DataFrame(data=[df[i].dropna().values for i in df.columns]).T
df1.index=dates
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
You can also use dropna on the columns as series
df1=pd.DataFrame(data=[df[i].dropna().values for i in df.columns]).T
df1.index=dates
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
answered Mar 25 at 17:00
G. AndersonG. Anderson
2,6371 gold badge6 silver badges13 bronze badges
2,6371 gold badge6 silver badges13 bronze badges
add a comment |
add a comment |
One tricky solution cause by you have duplicate index
pd.concat([df[x].dropna() for x in df.columns],1)
Out[24]:
ciao google microsoft
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
add a comment |
One tricky solution cause by you have duplicate index
pd.concat([df[x].dropna() for x in df.columns],1)
Out[24]:
ciao google microsoft
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
add a comment |
One tricky solution cause by you have duplicate index
pd.concat([df[x].dropna() for x in df.columns],1)
Out[24]:
ciao google microsoft
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
One tricky solution cause by you have duplicate index
pd.concat([df[x].dropna() for x in df.columns],1)
Out[24]:
ciao google microsoft
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 368000.0 37200000.0 135000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
SearchVolume 450000.0 37200000.0 110000.0
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
answered Mar 25 at 17:03
WeNYoBenWeNYoBen
144k8 gold badges51 silver badges80 bronze badges
144k8 gold badges51 silver badges80 bronze badges
add a comment |
add a comment |
My proposition is:
pd.DataFrame(data= colName: df[colName].dropna().values for colName in df.columns ,
index=['20140115', '20140215', '20140315', '20140415', '20140515', '20140615'])
The main point is a dictionary comprehension, executed for each column.
dropna removes NaN items and values allows to free oneself from
index values.
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
add a comment |
My proposition is:
pd.DataFrame(data= colName: df[colName].dropna().values for colName in df.columns ,
index=['20140115', '20140215', '20140315', '20140415', '20140515', '20140615'])
The main point is a dictionary comprehension, executed for each column.
dropna removes NaN items and values allows to free oneself from
index values.
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
add a comment |
My proposition is:
pd.DataFrame(data= colName: df[colName].dropna().values for colName in df.columns ,
index=['20140115', '20140215', '20140315', '20140415', '20140515', '20140615'])
The main point is a dictionary comprehension, executed for each column.
dropna removes NaN items and values allows to free oneself from
index values.
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
My proposition is:
pd.DataFrame(data= colName: df[colName].dropna().values for colName in df.columns ,
index=['20140115', '20140215', '20140315', '20140415', '20140515', '20140615'])
The main point is a dictionary comprehension, executed for each column.
dropna removes NaN items and values allows to free oneself from
index values.
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
edited Mar 25 at 17:08
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
answered Mar 25 at 17:02
Valdi_BoValdi_Bo
6,5812 gold badges9 silver badges16 bronze badges
6,5812 gold badges9 silver badges16 bronze badges
add a comment |
add a comment |
This should work:
denulled = col: df.loc[df[col].notnull(),col].values for col in df.columns
df_out = pd.DataFrame(denulled, index=date)
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
add a comment |
This should work:
denulled = col: df.loc[df[col].notnull(),col].values for col in df.columns
df_out = pd.DataFrame(denulled, index=date)
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
add a comment |
This should work:
denulled = col: df.loc[df[col].notnull(),col].values for col in df.columns
df_out = pd.DataFrame(denulled, index=date)
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
This should work:
denulled = col: df.loc[df[col].notnull(),col].values for col in df.columns
df_out = pd.DataFrame(denulled, index=date)
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
edited Mar 25 at 17:30
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
answered Mar 25 at 16:54
ags29ags29
1,1391 gold badge2 silver badges7 bronze badges
1,1391 gold badge2 silver badges7 bronze badges
add a comment |
add a comment |
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