Pandas Replace certain values in each column
0
I have a dataframe which looks as below
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| | Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| 0 | 6 | 148.0 | 72.0 | 35.0 | 125.0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85.0 | 66.0 | 29.0 | 125.0 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183.0 | 64.0 | 29.0 | 125.0 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89.0 | 66.0 | 23.0 | 94.0 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137.0 | 40.0 | 35.0 | 168.0 | 43.1 | 2.288 | 33 | 1 |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
After watching the box plot for each variable.I found that they have outliers in them.
So in each column except Outcome I want to replace the values which are greater than 95 percentile with value at 75 percentile and values which are less than 5 percentile with 25 percentile of that particular column
For example in column Glucose values which are above 95 percentile I want to replace them with value at 75 percentile of Glucose column
How can I do this with pandas filter and percentile function
Any help for this will be appreciated
python pandas percentile
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
add a comment |
0
I have a dataframe which looks as below
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| | Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| 0 | 6 | 148.0 | 72.0 | 35.0 | 125.0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85.0 | 66.0 | 29.0 | 125.0 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183.0 | 64.0 | 29.0 | 125.0 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89.0 | 66.0 | 23.0 | 94.0 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137.0 | 40.0 | 35.0 | 168.0 | 43.1 | 2.288 | 33 | 1 |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
After watching the box plot for each variable.I found that they have outliers in them.
So in each column except Outcome I want to replace the values which are greater than 95 percentile with value at 75 percentile and values which are less than 5 percentile with 25 percentile of that particular column
For example in column Glucose values which are above 95 percentile I want to replace them with value at 75 percentile of Glucose column
How can I do this with pandas filter and percentile function
Any help for this will be appreciated
python pandas percentile
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
add a comment |
0
0
0
I have a dataframe which looks as below
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| | Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| 0 | 6 | 148.0 | 72.0 | 35.0 | 125.0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85.0 | 66.0 | 29.0 | 125.0 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183.0 | 64.0 | 29.0 | 125.0 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89.0 | 66.0 | 23.0 | 94.0 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137.0 | 40.0 | 35.0 | 168.0 | 43.1 | 2.288 | 33 | 1 |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
After watching the box plot for each variable.I found that they have outliers in them.
So in each column except Outcome I want to replace the values which are greater than 95 percentile with value at 75 percentile and values which are less than 5 percentile with 25 percentile of that particular column
For example in column Glucose values which are above 95 percentile I want to replace them with value at 75 percentile of Glucose column
How can I do this with pandas filter and percentile function
Any help for this will be appreciated
python pandas percentile
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
I have a dataframe which looks as below
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| | Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
| 0 | 6 | 148.0 | 72.0 | 35.0 | 125.0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85.0 | 66.0 | 29.0 | 125.0 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183.0 | 64.0 | 29.0 | 125.0 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89.0 | 66.0 | 23.0 | 94.0 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137.0 | 40.0 | 35.0 | 168.0 | 43.1 | 2.288 | 33 | 1 |
+---+-------------+---------+---------------+---------------+---------+------+--------------------------+-----+----------+
After watching the box plot for each variable.I found that they have outliers in them.
So in each column except Outcome I want to replace the values which are greater than 95 percentile with value at 75 percentile and values which are less than 5 percentile with 25 percentile of that particular column
For example in column Glucose values which are above 95 percentile I want to replace them with value at 75 percentile of Glucose column
How can I do this with pandas filter and percentile function
Any help for this will be appreciated
python pandas percentile
python pandas percentile
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
asked Nov 13 '18 at 17:34
Rookie_123Rookie_123
410212
410212
add a comment |
add a comment |
1 Answer
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3
You can use apply on all columns except outcome, with the functions np.clip and np.percentile:
import numpy as np
percentile_df = df.set_index('Outcome').apply(lambda x: np.clip(x, *np.percentile(x, [25,75]))).reset_index()
>>> percentile_df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
[EDIT] I misread the question at first, here is a way to change the 5th and 95th percentile to 25th and 75th respectively, using np.select:
def cut(column):
conds = [column > np.percentile(column, 95),
column < np.percentile(column, 5)]
choices = [np.percentile(column, 75),
np.percentile(column, 25)]
return np.select(conds,choices,column)
df.set_index('Outcome',inplace=True)
df = df.apply(lambda x: cut(x)).reset_index()
>>> df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
answered Nov 13 '18 at 17:42
saculsacul
30k41740
1
I've never thought about it... why not? the documentation forpd.np.clipshould just be the docs fornp.clip. In any case, then I'll just delete thepd.
– sacul
Nov 13 '18 at 17:45
1
Never tried it myself, that's all. Also you can donp.percentile(x, [25, 75]). That version would get my vote :).
– jpp
Nov 13 '18 at 17:46
1
Thanks! Yeah, that's better!
– sacul
Nov 13 '18 at 17:48
What does*np.percentiledoes.I read aboutnp.percentilebut why*is used
– Rookie_123
Nov 13 '18 at 17:54
np.percentile(x, [25, 75])returns values for the 25th and the 75th percentile, so you need to unpack that using*so that the 25th percentile gets passed toa_minand the 75th percentile gets passed toa_maxofnp.clip
– sacul
Nov 13 '18 at 19:33
|
show 3 more comments
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1 Answer
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1 Answer
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3
You can use apply on all columns except outcome, with the functions np.clip and np.percentile:
import numpy as np
percentile_df = df.set_index('Outcome').apply(lambda x: np.clip(x, *np.percentile(x, [25,75]))).reset_index()
>>> percentile_df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
[EDIT] I misread the question at first, here is a way to change the 5th and 95th percentile to 25th and 75th respectively, using np.select:
def cut(column):
conds = [column > np.percentile(column, 95),
column < np.percentile(column, 5)]
choices = [np.percentile(column, 75),
np.percentile(column, 25)]
return np.select(conds,choices,column)
df.set_index('Outcome',inplace=True)
df = df.apply(lambda x: cut(x)).reset_index()
>>> df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
answered Nov 13 '18 at 17:42
saculsacul
30k41740
1
I've never thought about it... why not? the documentation forpd.np.clipshould just be the docs fornp.clip. In any case, then I'll just delete thepd.
– sacul
Nov 13 '18 at 17:45
1
Never tried it myself, that's all. Also you can donp.percentile(x, [25, 75]). That version would get my vote :).
– jpp
Nov 13 '18 at 17:46
1
Thanks! Yeah, that's better!
– sacul
Nov 13 '18 at 17:48
What does*np.percentiledoes.I read aboutnp.percentilebut why*is used
– Rookie_123
Nov 13 '18 at 17:54
np.percentile(x, [25, 75])returns values for the 25th and the 75th percentile, so you need to unpack that using*so that the 25th percentile gets passed toa_minand the 75th percentile gets passed toa_maxofnp.clip
– sacul
Nov 13 '18 at 19:33
|
show 3 more comments
3
You can use apply on all columns except outcome, with the functions np.clip and np.percentile:
import numpy as np
percentile_df = df.set_index('Outcome').apply(lambda x: np.clip(x, *np.percentile(x, [25,75]))).reset_index()
>>> percentile_df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
[EDIT] I misread the question at first, here is a way to change the 5th and 95th percentile to 25th and 75th respectively, using np.select:
def cut(column):
conds = [column > np.percentile(column, 95),
column < np.percentile(column, 5)]
choices = [np.percentile(column, 75),
np.percentile(column, 25)]
return np.select(conds,choices,column)
df.set_index('Outcome',inplace=True)
df = df.apply(lambda x: cut(x)).reset_index()
>>> df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
answered Nov 13 '18 at 17:42
saculsacul
30k41740
1
I've never thought about it... why not? the documentation forpd.np.clipshould just be the docs fornp.clip. In any case, then I'll just delete thepd.
– sacul
Nov 13 '18 at 17:45
1
Never tried it myself, that's all. Also you can donp.percentile(x, [25, 75]). That version would get my vote :).
– jpp
Nov 13 '18 at 17:46
1
Thanks! Yeah, that's better!
– sacul
Nov 13 '18 at 17:48
What does*np.percentiledoes.I read aboutnp.percentilebut why*is used
– Rookie_123
Nov 13 '18 at 17:54
np.percentile(x, [25, 75])returns values for the 25th and the 75th percentile, so you need to unpack that using*so that the 25th percentile gets passed toa_minand the 75th percentile gets passed toa_maxofnp.clip
– sacul
Nov 13 '18 at 19:33
|
show 3 more comments
3
3
3
You can use apply on all columns except outcome, with the functions np.clip and np.percentile:
import numpy as np
percentile_df = df.set_index('Outcome').apply(lambda x: np.clip(x, *np.percentile(x, [25,75]))).reset_index()
>>> percentile_df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
[EDIT] I misread the question at first, here is a way to change the 5th and 95th percentile to 25th and 75th respectively, using np.select:
def cut(column):
conds = [column > np.percentile(column, 95),
column < np.percentile(column, 5)]
choices = [np.percentile(column, 75),
np.percentile(column, 25)]
return np.select(conds,choices,column)
df.set_index('Outcome',inplace=True)
df = df.apply(lambda x: cut(x)).reset_index()
>>> df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
answered Nov 13 '18 at 17:42
saculsacul
30k41740
You can use apply on all columns except outcome, with the functions np.clip and np.percentile:
import numpy as np
percentile_df = df.set_index('Outcome').apply(lambda x: np.clip(x, *np.percentile(x, [25,75]))).reset_index()
>>> percentile_df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
[EDIT] I misread the question at first, here is a way to change the 5th and 95th percentile to 25th and 75th respectively, using np.select:
def cut(column):
conds = [column > np.percentile(column, 95),
column < np.percentile(column, 5)]
choices = [np.percentile(column, 75),
np.percentile(column, 25)]
return np.select(conds,choices,column)
df.set_index('Outcome',inplace=True)
df = df.apply(lambda x: cut(x)).reset_index()
>>> df
Outcome Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
0 1 6.0 148.0 66.0 35.0 125.0 33.6
1 0 1.0 89.0 66.0 29.0 125.0 26.6
2 1 6.0 148.0 64.0 29.0 125.0 26.6
3 0 1.0 89.0 66.0 29.0 125.0 28.1
4 1 1.0 137.0 64.0 35.0 125.0 33.6
DiabetesPedigreeFunction Age
0 0.627 33.0
1 0.351 31.0
2 0.672 32.0
3 0.351 31.0
4 0.672 33.0
answered Nov 13 '18 at 17:42
saculsacul
30k41740
edited Nov 14 '18 at 16:31
answered Nov 13 '18 at 17:42
saculsacul
30k41740
answered Nov 13 '18 at 17:42
saculsacul
30k41740
answered Nov 13 '18 at 17:42
saculsacul
30k41740
30k41740
1
I've never thought about it... why not? the documentation forpd.np.clipshould just be the docs fornp.clip. In any case, then I'll just delete thepd.
– sacul
Nov 13 '18 at 17:45
1
Never tried it myself, that's all. Also you can donp.percentile(x, [25, 75]). That version would get my vote :).
– jpp
Nov 13 '18 at 17:46
1
Thanks! Yeah, that's better!
– sacul
Nov 13 '18 at 17:48
What does*np.percentiledoes.I read aboutnp.percentilebut why*is used
– Rookie_123
Nov 13 '18 at 17:54
np.percentile(x, [25, 75])returns values for the 25th and the 75th percentile, so you need to unpack that using*so that the 25th percentile gets passed toa_minand the 75th percentile gets passed toa_maxofnp.clip
– sacul
Nov 13 '18 at 19:33
|
show 3 more comments
1
I've never thought about it... why not? the documentation forpd.np.clipshould just be the docs fornp.clip. In any case, then I'll just delete thepd.
– sacul
Nov 13 '18 at 17:45
1
Never tried it myself, that's all. Also you can donp.percentile(x, [25, 75]). That version would get my vote :).
– jpp
Nov 13 '18 at 17:46
1
Thanks! Yeah, that's better!
– sacul
Nov 13 '18 at 17:48
What does*np.percentiledoes.I read aboutnp.percentilebut why*is used
– Rookie_123
Nov 13 '18 at 17:54
np.percentile(x, [25, 75])returns values for the 25th and the 75th percentile, so you need to unpack that using*so that the 25th percentile gets passed toa_minand the 75th percentile gets passed toa_maxofnp.clip
– sacul
Nov 13 '18 at 19:33
1
1
I've never thought about it... why not? the documentation for
pd.np.clip should just be the docs for np.clip. In any case, then I'll just delete the pd.– sacul
Nov 13 '18 at 17:45
I've never thought about it... why not? the documentation for
pd.np.clip should just be the docs for np.clip. In any case, then I'll just delete the pd.– sacul
Nov 13 '18 at 17:45
1
1
Never tried it myself, that's all. Also you can do
np.percentile(x, [25, 75]). That version would get my vote :).– jpp
Nov 13 '18 at 17:46
Never tried it myself, that's all. Also you can do
np.percentile(x, [25, 75]). That version would get my vote :).– jpp
Nov 13 '18 at 17:46
1
1
Thanks! Yeah, that's better!
– sacul
Nov 13 '18 at 17:48
Thanks! Yeah, that's better!
– sacul
Nov 13 '18 at 17:48
What does
*np.percentile does.I read about np.percentile but why * is used– Rookie_123
Nov 13 '18 at 17:54
What does
*np.percentile does.I read about np.percentile but why * is used– Rookie_123
Nov 13 '18 at 17:54
np.percentile(x, [25, 75]) returns values for the 25th and the 75th percentile, so you need to unpack that using * so that the 25th percentile gets passed to a_min and the 75th percentile gets passed to a_max of np.clip– sacul
Nov 13 '18 at 19:33
np.percentile(x, [25, 75]) returns values for the 25th and the 75th percentile, so you need to unpack that using * so that the 25th percentile gets passed to a_min and the 75th percentile gets passed to a_max of np.clip– sacul
Nov 13 '18 at 19:33
|
show 3 more comments
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