Replace zeros with last value different from zero
2
I have the following dataframe:
print(inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 0 0
3 12/09/18 2 0 0
4 13/09/18 0 0 0
5 14/09/18 4 30 1
I would like to change the values equal to zero, with the last value != from zero, in each columns, as:
print(final_inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
How could I do it?
python pandas
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
add a comment |
2
I have the following dataframe:
print(inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 0 0
3 12/09/18 2 0 0
4 13/09/18 0 0 0
5 14/09/18 4 30 1
I would like to change the values equal to zero, with the last value != from zero, in each columns, as:
print(final_inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
How could I do it?
python pandas
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
add a comment |
2
2
2
I have the following dataframe:
print(inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 0 0
3 12/09/18 2 0 0
4 13/09/18 0 0 0
5 14/09/18 4 30 1
I would like to change the values equal to zero, with the last value != from zero, in each columns, as:
print(final_inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
How could I do it?
python pandas
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
I have the following dataframe:
print(inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 0 0
3 12/09/18 2 0 0
4 13/09/18 0 0 0
5 14/09/18 4 30 1
I would like to change the values equal to zero, with the last value != from zero, in each columns, as:
print(final_inventory_df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
How could I do it?
python pandas
python pandas
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
asked Nov 23 '18 at 9:46
Alessandro CeccarelliAlessandro Ceccarelli
252211
252211
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
5
Idea is replace 0 to NaNs by mask and then forward filling them by previous non missing values:
cols = df.columns.difference(['dt_op'])
df[cols] = df[cols].mask(df[cols] == 0).ffill().astype(int)
Similar solution with numpy.where:
df[cols] = pd.DataFrame(np.where(df[cols] == 0, np.nan, df[cols]),
index=df.index,
columns=cols).ffill().astype(int)
print (df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
Solution for fun - convert to integer all columns without dt_op:
d = dict.fromkeys(df.columns.difference(['dt_op']), 'int')
df = df.mask(df == 0).ffill().astype(d)
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
1
Another excellent piece +1
– pygo
Nov 23 '18 at 10:45
What if it reports: ValueError: Cannot convert non-finite values (NA or inf) to integer? @jezrael
– Alessandro Ceccarelli
Nov 23 '18 at 14:16
@AlessandroCeccarelli - It means there is someNaNvalue, so only remove.astype(int)
– jezrael
Nov 23 '18 at 14:17
@AlessandroCeccarelli - BecauseNaNisfloattype and cannot be casted to integers, so raise error
– jezrael
Nov 23 '18 at 14:18
1
Now it is working properly! Many thanks
– Alessandro Ceccarelli
Nov 23 '18 at 14:35
|
show 6 more comments
2
Just another option:
df.iloc[:,1:] = df.iloc[:,1:].replace(0, np.nan).ffill().astype(int)
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
1
This is an excellent show with slicing +1
– pygo
Nov 23 '18 at 10:46
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
5
Idea is replace 0 to NaNs by mask and then forward filling them by previous non missing values:
cols = df.columns.difference(['dt_op'])
df[cols] = df[cols].mask(df[cols] == 0).ffill().astype(int)
Similar solution with numpy.where:
df[cols] = pd.DataFrame(np.where(df[cols] == 0, np.nan, df[cols]),
index=df.index,
columns=cols).ffill().astype(int)
print (df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
Solution for fun - convert to integer all columns without dt_op:
d = dict.fromkeys(df.columns.difference(['dt_op']), 'int')
df = df.mask(df == 0).ffill().astype(d)
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
1
Another excellent piece +1
– pygo
Nov 23 '18 at 10:45
What if it reports: ValueError: Cannot convert non-finite values (NA or inf) to integer? @jezrael
– Alessandro Ceccarelli
Nov 23 '18 at 14:16
@AlessandroCeccarelli - It means there is someNaNvalue, so only remove.astype(int)
– jezrael
Nov 23 '18 at 14:17
@AlessandroCeccarelli - BecauseNaNisfloattype and cannot be casted to integers, so raise error
– jezrael
Nov 23 '18 at 14:18
1
Now it is working properly! Many thanks
– Alessandro Ceccarelli
Nov 23 '18 at 14:35
|
show 6 more comments
5
Idea is replace 0 to NaNs by mask and then forward filling them by previous non missing values:
cols = df.columns.difference(['dt_op'])
df[cols] = df[cols].mask(df[cols] == 0).ffill().astype(int)
Similar solution with numpy.where:
df[cols] = pd.DataFrame(np.where(df[cols] == 0, np.nan, df[cols]),
index=df.index,
columns=cols).ffill().astype(int)
print (df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
Solution for fun - convert to integer all columns without dt_op:
d = dict.fromkeys(df.columns.difference(['dt_op']), 'int')
df = df.mask(df == 0).ffill().astype(d)
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
1
Another excellent piece +1
– pygo
Nov 23 '18 at 10:45
What if it reports: ValueError: Cannot convert non-finite values (NA or inf) to integer? @jezrael
– Alessandro Ceccarelli
Nov 23 '18 at 14:16
@AlessandroCeccarelli - It means there is someNaNvalue, so only remove.astype(int)
– jezrael
Nov 23 '18 at 14:17
@AlessandroCeccarelli - BecauseNaNisfloattype and cannot be casted to integers, so raise error
– jezrael
Nov 23 '18 at 14:18
1
Now it is working properly! Many thanks
– Alessandro Ceccarelli
Nov 23 '18 at 14:35
|
show 6 more comments
5
5
5
Idea is replace 0 to NaNs by mask and then forward filling them by previous non missing values:
cols = df.columns.difference(['dt_op'])
df[cols] = df[cols].mask(df[cols] == 0).ffill().astype(int)
Similar solution with numpy.where:
df[cols] = pd.DataFrame(np.where(df[cols] == 0, np.nan, df[cols]),
index=df.index,
columns=cols).ffill().astype(int)
print (df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
Solution for fun - convert to integer all columns without dt_op:
d = dict.fromkeys(df.columns.difference(['dt_op']), 'int')
df = df.mask(df == 0).ffill().astype(d)
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
Idea is replace 0 to NaNs by mask and then forward filling them by previous non missing values:
cols = df.columns.difference(['dt_op'])
df[cols] = df[cols].mask(df[cols] == 0).ffill().astype(int)
Similar solution with numpy.where:
df[cols] = pd.DataFrame(np.where(df[cols] == 0, np.nan, df[cols]),
index=df.index,
columns=cols).ffill().astype(int)
print (df)
dt_op Prod_1 Prod_2 Prod_n
1 10/09/18 5 50 2
2 11/09/18 4 50 2
3 12/09/18 2 50 2
4 13/09/18 2 50 2
5 14/09/18 4 30 1
Solution for fun - convert to integer all columns without dt_op:
d = dict.fromkeys(df.columns.difference(['dt_op']), 'int')
df = df.mask(df == 0).ffill().astype(d)
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
edited Nov 23 '18 at 9:53
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
answered Nov 23 '18 at 9:47
jezraeljezrael
323k23265342
323k23265342
1
Another excellent piece +1
– pygo
Nov 23 '18 at 10:45
What if it reports: ValueError: Cannot convert non-finite values (NA or inf) to integer? @jezrael
– Alessandro Ceccarelli
Nov 23 '18 at 14:16
@AlessandroCeccarelli - It means there is someNaNvalue, so only remove.astype(int)
– jezrael
Nov 23 '18 at 14:17
@AlessandroCeccarelli - BecauseNaNisfloattype and cannot be casted to integers, so raise error
– jezrael
Nov 23 '18 at 14:18
1
Now it is working properly! Many thanks
– Alessandro Ceccarelli
Nov 23 '18 at 14:35
|
show 6 more comments
1
Another excellent piece +1
– pygo
Nov 23 '18 at 10:45
What if it reports: ValueError: Cannot convert non-finite values (NA or inf) to integer? @jezrael
– Alessandro Ceccarelli
Nov 23 '18 at 14:16
@AlessandroCeccarelli - It means there is someNaNvalue, so only remove.astype(int)
– jezrael
Nov 23 '18 at 14:17
@AlessandroCeccarelli - BecauseNaNisfloattype and cannot be casted to integers, so raise error
– jezrael
Nov 23 '18 at 14:18
1
Now it is working properly! Many thanks
– Alessandro Ceccarelli
Nov 23 '18 at 14:35
1
1
Another excellent piece +1
– pygo
Nov 23 '18 at 10:45
Another excellent piece +1
– pygo
Nov 23 '18 at 10:45
What if it reports: ValueError: Cannot convert non-finite values (NA or inf) to integer? @jezrael
– Alessandro Ceccarelli
Nov 23 '18 at 14:16
What if it reports: ValueError: Cannot convert non-finite values (NA or inf) to integer? @jezrael
– Alessandro Ceccarelli
Nov 23 '18 at 14:16
@AlessandroCeccarelli - It means there is some
NaN value, so only remove .astype(int)– jezrael
Nov 23 '18 at 14:17
@AlessandroCeccarelli - It means there is some
NaN value, so only remove .astype(int)– jezrael
Nov 23 '18 at 14:17
@AlessandroCeccarelli - Because
NaN is float type and cannot be casted to integers, so raise error– jezrael
Nov 23 '18 at 14:18
@AlessandroCeccarelli - Because
NaN is float type and cannot be casted to integers, so raise error– jezrael
Nov 23 '18 at 14:18
1
1
Now it is working properly! Many thanks
– Alessandro Ceccarelli
Nov 23 '18 at 14:35
Now it is working properly! Many thanks
– Alessandro Ceccarelli
Nov 23 '18 at 14:35
|
show 6 more comments
2
Just another option:
df.iloc[:,1:] = df.iloc[:,1:].replace(0, np.nan).ffill().astype(int)
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
1
This is an excellent show with slicing +1
– pygo
Nov 23 '18 at 10:46
add a comment |
2
Just another option:
df.iloc[:,1:] = df.iloc[:,1:].replace(0, np.nan).ffill().astype(int)
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
1
This is an excellent show with slicing +1
– pygo
Nov 23 '18 at 10:46
add a comment |
2
2
2
Just another option:
df.iloc[:,1:] = df.iloc[:,1:].replace(0, np.nan).ffill().astype(int)
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
Just another option:
df.iloc[:,1:] = df.iloc[:,1:].replace(0, np.nan).ffill().astype(int)
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
edited Nov 23 '18 at 10:47
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
answered Nov 23 '18 at 10:22
JoeJoe
5,89621129
5,89621129
1
This is an excellent show with slicing +1
– pygo
Nov 23 '18 at 10:46
add a comment |
1
This is an excellent show with slicing +1
– pygo
Nov 23 '18 at 10:46
1
1
This is an excellent show with slicing +1
– pygo
Nov 23 '18 at 10:46
This is an excellent show with slicing +1
– pygo
Nov 23 '18 at 10:46
add a comment |
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