scikit-learn StandardScaler() returns partly NaNs
1
I got stuck with an issue when I apply preprocessing.StandardScaler() to a Pandas DataFrame.
df['total_price'].describe()
returns
count 24895.000000
mean 216.377369
std 161.246931
min 0.000000
25% 109.900000
50% 174.000000
75% 273.000000
max 1355.900000
Name: total_price, dtype: float64
Strangely, after I run
x = df[['total_price']]
standard_scaler = preprocessing.StandardScaler()
x_scaled = standard_scaler.fit_transform(x)
df['new_col'] = pd.DataFrame(x_scaled)
my new column with the standardized values contains also NaNs:
df[['total_price', 'new_col']].head()
total_price new_col
0 241.95 0.158596
1 241.95 0.158596
2 241.95 0.158596
3 81.95 -0.833691
4 81.95 -0.833691
df[['total_price', 'new_col']].tail()
total_price new_col
28167 264.0 NaN
28168 264.0 NaN
28176 94.0 NaN
28177 166.0 NaN
28178 166.0 NaN
What's going wrong here?
python pandas scikit-learn
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
add a comment |
1
I got stuck with an issue when I apply preprocessing.StandardScaler() to a Pandas DataFrame.
df['total_price'].describe()
returns
count 24895.000000
mean 216.377369
std 161.246931
min 0.000000
25% 109.900000
50% 174.000000
75% 273.000000
max 1355.900000
Name: total_price, dtype: float64
Strangely, after I run
x = df[['total_price']]
standard_scaler = preprocessing.StandardScaler()
x_scaled = standard_scaler.fit_transform(x)
df['new_col'] = pd.DataFrame(x_scaled)
my new column with the standardized values contains also NaNs:
df[['total_price', 'new_col']].head()
total_price new_col
0 241.95 0.158596
1 241.95 0.158596
2 241.95 0.158596
3 81.95 -0.833691
4 81.95 -0.833691
df[['total_price', 'new_col']].tail()
total_price new_col
28167 264.0 NaN
28168 264.0 NaN
28176 94.0 NaN
28177 166.0 NaN
28178 166.0 NaN
What's going wrong here?
python pandas scikit-learn
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
1
Your original column had24895entries, and your new DF has indices going all the way to28178, so my first guess that some sort of join or concatenation may have resulted in an index mismatch between the old and new DFs. Were there any intermediate steps not shown, like a train-test split?
– G. Anderson
Nov 14 '18 at 19:15
it's part of a larger df and I removed rows before. But this was not inbetween the steps above
– zinyosrim
Nov 14 '18 at 19:39
After reading your comment I did adf = df.reset_index()and the problem got resolved
– zinyosrim
Nov 14 '18 at 19:49
Glad I could help
– G. Anderson
Nov 14 '18 at 20:01
add a comment |
1
1
1
I got stuck with an issue when I apply preprocessing.StandardScaler() to a Pandas DataFrame.
df['total_price'].describe()
returns
count 24895.000000
mean 216.377369
std 161.246931
min 0.000000
25% 109.900000
50% 174.000000
75% 273.000000
max 1355.900000
Name: total_price, dtype: float64
Strangely, after I run
x = df[['total_price']]
standard_scaler = preprocessing.StandardScaler()
x_scaled = standard_scaler.fit_transform(x)
df['new_col'] = pd.DataFrame(x_scaled)
my new column with the standardized values contains also NaNs:
df[['total_price', 'new_col']].head()
total_price new_col
0 241.95 0.158596
1 241.95 0.158596
2 241.95 0.158596
3 81.95 -0.833691
4 81.95 -0.833691
df[['total_price', 'new_col']].tail()
total_price new_col
28167 264.0 NaN
28168 264.0 NaN
28176 94.0 NaN
28177 166.0 NaN
28178 166.0 NaN
What's going wrong here?
python pandas scikit-learn
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
I got stuck with an issue when I apply preprocessing.StandardScaler() to a Pandas DataFrame.
df['total_price'].describe()
returns
count 24895.000000
mean 216.377369
std 161.246931
min 0.000000
25% 109.900000
50% 174.000000
75% 273.000000
max 1355.900000
Name: total_price, dtype: float64
Strangely, after I run
x = df[['total_price']]
standard_scaler = preprocessing.StandardScaler()
x_scaled = standard_scaler.fit_transform(x)
df['new_col'] = pd.DataFrame(x_scaled)
my new column with the standardized values contains also NaNs:
df[['total_price', 'new_col']].head()
total_price new_col
0 241.95 0.158596
1 241.95 0.158596
2 241.95 0.158596
3 81.95 -0.833691
4 81.95 -0.833691
df[['total_price', 'new_col']].tail()
total_price new_col
28167 264.0 NaN
28168 264.0 NaN
28176 94.0 NaN
28177 166.0 NaN
28178 166.0 NaN
What's going wrong here?
python pandas scikit-learn
python pandas scikit-learn
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
asked Nov 14 '18 at 18:44
zinyosrimzinyosrim
438414
438414
1
Your original column had24895entries, and your new DF has indices going all the way to28178, so my first guess that some sort of join or concatenation may have resulted in an index mismatch between the old and new DFs. Were there any intermediate steps not shown, like a train-test split?
– G. Anderson
Nov 14 '18 at 19:15
it's part of a larger df and I removed rows before. But this was not inbetween the steps above
– zinyosrim
Nov 14 '18 at 19:39
After reading your comment I did adf = df.reset_index()and the problem got resolved
– zinyosrim
Nov 14 '18 at 19:49
Glad I could help
– G. Anderson
Nov 14 '18 at 20:01
add a comment |
1
Your original column had24895entries, and your new DF has indices going all the way to28178, so my first guess that some sort of join or concatenation may have resulted in an index mismatch between the old and new DFs. Were there any intermediate steps not shown, like a train-test split?
– G. Anderson
Nov 14 '18 at 19:15
it's part of a larger df and I removed rows before. But this was not inbetween the steps above
– zinyosrim
Nov 14 '18 at 19:39
After reading your comment I did adf = df.reset_index()and the problem got resolved
– zinyosrim
Nov 14 '18 at 19:49
Glad I could help
– G. Anderson
Nov 14 '18 at 20:01
1
1
Your original column had
24895 entries, and your new DF has indices going all the way to 28178, so my first guess that some sort of join or concatenation may have resulted in an index mismatch between the old and new DFs. Were there any intermediate steps not shown, like a train-test split?– G. Anderson
Nov 14 '18 at 19:15
Your original column had
24895 entries, and your new DF has indices going all the way to 28178, so my first guess that some sort of join or concatenation may have resulted in an index mismatch between the old and new DFs. Were there any intermediate steps not shown, like a train-test split?– G. Anderson
Nov 14 '18 at 19:15
it's part of a larger df and I removed rows before. But this was not inbetween the steps above
– zinyosrim
Nov 14 '18 at 19:39
it's part of a larger df and I removed rows before. But this was not inbetween the steps above
– zinyosrim
Nov 14 '18 at 19:39
After reading your comment I did a
df = df.reset_index() and the problem got resolved– zinyosrim
Nov 14 '18 at 19:49
After reading your comment I did a
df = df.reset_index() and the problem got resolved– zinyosrim
Nov 14 '18 at 19:49
Glad I could help
– G. Anderson
Nov 14 '18 at 20:01
Glad I could help
– G. Anderson
Nov 14 '18 at 20:01
add a comment |
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1
Your original column had
24895entries, and your new DF has indices going all the way to28178, so my first guess that some sort of join or concatenation may have resulted in an index mismatch between the old and new DFs. Were there any intermediate steps not shown, like a train-test split?– G. Anderson
Nov 14 '18 at 19:15
it's part of a larger df and I removed rows before. But this was not inbetween the steps above
– zinyosrim
Nov 14 '18 at 19:39
After reading your comment I did a
df = df.reset_index()and the problem got resolved– zinyosrim
Nov 14 '18 at 19:49
Glad I could help
– G. Anderson
Nov 14 '18 at 20:01