df.fillna() not working but df.dropna() working
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In my code the df.fillna() method is not working when the df.dropna() method is working. I don't want to drop the column though. What can I do that the fillna() method works?
def preprocess_df(df):
for col in df.columns: # go through all of the columns
if col != "target": # normalize all ... except for the target itself!
df[col] = df[col].pct_change() # pct change "normalizes" the different currencies (each crypto coin has vastly diff values, we're really more interested in the other coin's movements)
# df.dropna(inplace=True) # remove the nas created by pct_change
df.fillna(method="ffill", inplace=True)
print(df)
break
df[col] = preprocessing.scale(df[col].values) # scale between 0 and 1.
python pandas
asked Nov 22 at 14:11
user9468014
358
|
show 1 more comment
up vote
0
down vote
favorite
In my code the df.fillna() method is not working when the df.dropna() method is working. I don't want to drop the column though. What can I do that the fillna() method works?
def preprocess_df(df):
for col in df.columns: # go through all of the columns
if col != "target": # normalize all ... except for the target itself!
df[col] = df[col].pct_change() # pct change "normalizes" the different currencies (each crypto coin has vastly diff values, we're really more interested in the other coin's movements)
# df.dropna(inplace=True) # remove the nas created by pct_change
df.fillna(method="ffill", inplace=True)
print(df)
break
df[col] = preprocessing.scale(df[col].values) # scale between 0 and 1.
python pandas
asked Nov 22 at 14:11
user9468014
358
Trydf.fillna(method="ffill", inplace=True, axis=1)
– roganjosh
Nov 22 at 14:16
3
What do you mean by "not working"? Are you getting an error? If so, what's the error message?
– Lukas Thaler
Nov 22 at 14:16
the NaN does not disappear.
– user9468014
Nov 22 at 14:18
I've tried with df.fillna(method="ffill", inplace=True, axis=1) but still the same.
– user9468014
Nov 22 at 14:18
Error message is: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
– user9468014
Nov 22 at 14:20
|
show 1 more comment
up vote
0
down vote
favorite
up vote
0
down vote
favorite
In my code the df.fillna() method is not working when the df.dropna() method is working. I don't want to drop the column though. What can I do that the fillna() method works?
def preprocess_df(df):
for col in df.columns: # go through all of the columns
if col != "target": # normalize all ... except for the target itself!
df[col] = df[col].pct_change() # pct change "normalizes" the different currencies (each crypto coin has vastly diff values, we're really more interested in the other coin's movements)
# df.dropna(inplace=True) # remove the nas created by pct_change
df.fillna(method="ffill", inplace=True)
print(df)
break
df[col] = preprocessing.scale(df[col].values) # scale between 0 and 1.
python pandas
asked Nov 22 at 14:11
user9468014
358
In my code the df.fillna() method is not working when the df.dropna() method is working. I don't want to drop the column though. What can I do that the fillna() method works?
def preprocess_df(df):
for col in df.columns: # go through all of the columns
if col != "target": # normalize all ... except for the target itself!
df[col] = df[col].pct_change() # pct change "normalizes" the different currencies (each crypto coin has vastly diff values, we're really more interested in the other coin's movements)
# df.dropna(inplace=True) # remove the nas created by pct_change
df.fillna(method="ffill", inplace=True)
print(df)
break
df[col] = preprocessing.scale(df[col].values) # scale between 0 and 1.
python pandas
python pandas
asked Nov 22 at 14:11
user9468014
358
asked Nov 22 at 14:11
user9468014
358
asked Nov 22 at 14:11
user9468014
358
asked Nov 22 at 14:11
user9468014
358
asked Nov 22 at 14:11
user9468014
358
358
Trydf.fillna(method="ffill", inplace=True, axis=1)
– roganjosh
Nov 22 at 14:16
3
What do you mean by "not working"? Are you getting an error? If so, what's the error message?
– Lukas Thaler
Nov 22 at 14:16
the NaN does not disappear.
– user9468014
Nov 22 at 14:18
I've tried with df.fillna(method="ffill", inplace=True, axis=1) but still the same.
– user9468014
Nov 22 at 14:18
Error message is: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
– user9468014
Nov 22 at 14:20
|
show 1 more comment
Trydf.fillna(method="ffill", inplace=True, axis=1)
– roganjosh
Nov 22 at 14:16
3
What do you mean by "not working"? Are you getting an error? If so, what's the error message?
– Lukas Thaler
Nov 22 at 14:16
the NaN does not disappear.
– user9468014
Nov 22 at 14:18
I've tried with df.fillna(method="ffill", inplace=True, axis=1) but still the same.
– user9468014
Nov 22 at 14:18
Error message is: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
– user9468014
Nov 22 at 14:20
Try
df.fillna(method="ffill", inplace=True, axis=1)– roganjosh
Nov 22 at 14:16
Try
df.fillna(method="ffill", inplace=True, axis=1)– roganjosh
Nov 22 at 14:16
3
3
What do you mean by "not working"? Are you getting an error? If so, what's the error message?
– Lukas Thaler
Nov 22 at 14:16
What do you mean by "not working"? Are you getting an error? If so, what's the error message?
– Lukas Thaler
Nov 22 at 14:16
the NaN does not disappear.
– user9468014
Nov 22 at 14:18
the NaN does not disappear.
– user9468014
Nov 22 at 14:18
I've tried with df.fillna(method="ffill", inplace=True, axis=1) but still the same.
– user9468014
Nov 22 at 14:18
I've tried with df.fillna(method="ffill", inplace=True, axis=1) but still the same.
– user9468014
Nov 22 at 14:18
Error message is: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
– user9468014
Nov 22 at 14:20
Error message is: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
– user9468014
Nov 22 at 14:20
|
show 1 more comment
1 Answer
1
active
oldest
votes
up vote
0
down vote
You were almost there:
df = df.fillna(method="ffill", inplace=True)
You have to assign it back to df
answered Nov 22 at 15:01
erncyp
515
3
This is incorrect. You should not assign back when you useinplace=True.
– jpp
Nov 22 at 15:09
not working unfortunetally :(
– user9468014
Nov 22 at 22:45
My bad @jpp, you are correct. Maybe it is something to do withmethod='ffill'? According to pd docs,ffillpropagates the last valid observation. What if the entire column is NaN s. How does it know what to propagate it with? Perhaps its worth manually telling it what to fill the NaNs with?
– erncyp
Nov 23 at 9:15
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
You were almost there:
df = df.fillna(method="ffill", inplace=True)
You have to assign it back to df
answered Nov 22 at 15:01
erncyp
515
3
This is incorrect. You should not assign back when you useinplace=True.
– jpp
Nov 22 at 15:09
not working unfortunetally :(
– user9468014
Nov 22 at 22:45
My bad @jpp, you are correct. Maybe it is something to do withmethod='ffill'? According to pd docs,ffillpropagates the last valid observation. What if the entire column is NaN s. How does it know what to propagate it with? Perhaps its worth manually telling it what to fill the NaNs with?
– erncyp
Nov 23 at 9:15
add a comment |
up vote
0
down vote
You were almost there:
df = df.fillna(method="ffill", inplace=True)
You have to assign it back to df
answered Nov 22 at 15:01
erncyp
515
3
This is incorrect. You should not assign back when you useinplace=True.
– jpp
Nov 22 at 15:09
not working unfortunetally :(
– user9468014
Nov 22 at 22:45
My bad @jpp, you are correct. Maybe it is something to do withmethod='ffill'? According to pd docs,ffillpropagates the last valid observation. What if the entire column is NaN s. How does it know what to propagate it with? Perhaps its worth manually telling it what to fill the NaNs with?
– erncyp
Nov 23 at 9:15
add a comment |
up vote
0
down vote
up vote
0
down vote
You were almost there:
df = df.fillna(method="ffill", inplace=True)
You have to assign it back to df
answered Nov 22 at 15:01
erncyp
515
You were almost there:
df = df.fillna(method="ffill", inplace=True)
You have to assign it back to df
answered Nov 22 at 15:01
erncyp
515
answered Nov 22 at 15:01
erncyp
515
answered Nov 22 at 15:01
erncyp
515
answered Nov 22 at 15:01
erncyp
515
515
3
This is incorrect. You should not assign back when you useinplace=True.
– jpp
Nov 22 at 15:09
not working unfortunetally :(
– user9468014
Nov 22 at 22:45
My bad @jpp, you are correct. Maybe it is something to do withmethod='ffill'? According to pd docs,ffillpropagates the last valid observation. What if the entire column is NaN s. How does it know what to propagate it with? Perhaps its worth manually telling it what to fill the NaNs with?
– erncyp
Nov 23 at 9:15
add a comment |
3
This is incorrect. You should not assign back when you useinplace=True.
– jpp
Nov 22 at 15:09
not working unfortunetally :(
– user9468014
Nov 22 at 22:45
My bad @jpp, you are correct. Maybe it is something to do withmethod='ffill'? According to pd docs,ffillpropagates the last valid observation. What if the entire column is NaN s. How does it know what to propagate it with? Perhaps its worth manually telling it what to fill the NaNs with?
– erncyp
Nov 23 at 9:15
3
3
This is incorrect. You should not assign back when you use
inplace=True.– jpp
Nov 22 at 15:09
This is incorrect. You should not assign back when you use
inplace=True.– jpp
Nov 22 at 15:09
not working unfortunetally :(
– user9468014
Nov 22 at 22:45
not working unfortunetally :(
– user9468014
Nov 22 at 22:45
My bad @jpp, you are correct. Maybe it is something to do with
method='ffill'? According to pd docs, ffill propagates the last valid observation. What if the entire column is NaN s. How does it know what to propagate it with? Perhaps its worth manually telling it what to fill the NaNs with?– erncyp
Nov 23 at 9:15
My bad @jpp, you are correct. Maybe it is something to do with
method='ffill'? According to pd docs, ffill propagates the last valid observation. What if the entire column is NaN s. How does it know what to propagate it with? Perhaps its worth manually telling it what to fill the NaNs with?– erncyp
Nov 23 at 9:15
add a comment |
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df.fillna(method="ffill", inplace=True, axis=1)– roganjosh
Nov 22 at 14:16
3
What do you mean by "not working"? Are you getting an error? If so, what's the error message?
– Lukas Thaler
Nov 22 at 14:16
the NaN does not disappear.
– user9468014
Nov 22 at 14:18
I've tried with df.fillna(method="ffill", inplace=True, axis=1) but still the same.
– user9468014
Nov 22 at 14:18
Error message is: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
– user9468014
Nov 22 at 14:20