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My small script on value alteration in columns of a data not working

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0

$begingroup$

I have a data set which has "Speed" as one of the columns (features). The column contains both zero and non-zero values. I want to randomly set 10% of the non-zero values to zeros. This will change the corresponding "class" label to zeros. I mean any value set to zero, its corresponding class value will be zero as well. I have done this but it is give me errors. Though due to error, I cannot tell it will give me the update/result I want.

file_path = 'Processed_data/data1.csv' 
df = pd.read_csv(file_path) 
per_change = 0.1 
attr = 'Speed' 
target = 'Class' 
df_spd = df[df['Speed'] > 0.]

num_rows_to_change = int(df.shape[0] * per_change) 
num_with_zero_initial = df[df[attr] == 0].shape[0] 
assert df_spd.shape[0] > num_rows_to_change, 
 'Number of rows with non-zero speed is less than 10% of the original dataset.' 
df_update = df_spd.sample(num_rows_to_change) 
df_update[attr] = 0. 
df_update[target] = 0. 
df.update(df_update) 
update_list = df_update.index.tolist() 
num_with_zero_final = df[df['Speed'] == 0].shape[0] 
assert num_with_zero_final == num_with_zero_initial + num_rows_to_change, 
 'Number of rows needed to change not equal to number of rows changed.' 
df.to_csv('changed.csv')

pandas

share|improve this question

edited 2 days ago

Kiritee Gak

1,2991420

asked 2 days ago

elvin ugonnaelvin ugonna

11

New contributor

elvin ugonna is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Please write the error of the presented code.
  $endgroup$
  – Alireza Zolanvari
  2 days ago
  

  
  
  
  

add a comment | 

0

$begingroup$

I have a data set which has "Speed" as one of the columns (features). The column contains both zero and non-zero values. I want to randomly set 10% of the non-zero values to zeros. This will change the corresponding "class" label to zeros. I mean any value set to zero, its corresponding class value will be zero as well. I have done this but it is give me errors. Though due to error, I cannot tell it will give me the update/result I want.

file_path = 'Processed_data/data1.csv' 
df = pd.read_csv(file_path) 
per_change = 0.1 
attr = 'Speed' 
target = 'Class' 
df_spd = df[df['Speed'] > 0.]

num_rows_to_change = int(df.shape[0] * per_change) 
num_with_zero_initial = df[df[attr] == 0].shape[0] 
assert df_spd.shape[0] > num_rows_to_change, 
 'Number of rows with non-zero speed is less than 10% of the original dataset.' 
df_update = df_spd.sample(num_rows_to_change) 
df_update[attr] = 0. 
df_update[target] = 0. 
df.update(df_update) 
update_list = df_update.index.tolist() 
num_with_zero_final = df[df['Speed'] == 0].shape[0] 
assert num_with_zero_final == num_with_zero_initial + num_rows_to_change, 
 'Number of rows needed to change not equal to number of rows changed.' 
df.to_csv('changed.csv')

pandas

share|improve this question

edited 2 days ago

Kiritee Gak

1,2991420

asked 2 days ago

elvin ugonnaelvin ugonna

11

New contributor

elvin ugonna is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Please write the error of the presented code.
  $endgroup$
  – Alireza Zolanvari
  2 days ago
  

  
  
  
  

add a comment | 

$begingroup$

I have a data set which has "Speed" as one of the columns (features). The column contains both zero and non-zero values. I want to randomly set 10% of the non-zero values to zeros. This will change the corresponding "class" label to zeros. I mean any value set to zero, its corresponding class value will be zero as well. I have done this but it is give me errors. Though due to error, I cannot tell it will give me the update/result I want.

file_path = 'Processed_data/data1.csv' 
df = pd.read_csv(file_path) 
per_change = 0.1 
attr = 'Speed' 
target = 'Class' 
df_spd = df[df['Speed'] > 0.]

num_rows_to_change = int(df.shape[0] * per_change) 
num_with_zero_initial = df[df[attr] == 0].shape[0] 
assert df_spd.shape[0] > num_rows_to_change, 
 'Number of rows with non-zero speed is less than 10% of the original dataset.' 
df_update = df_spd.sample(num_rows_to_change) 
df_update[attr] = 0. 
df_update[target] = 0. 
df.update(df_update) 
update_list = df_update.index.tolist() 
num_with_zero_final = df[df['Speed'] == 0].shape[0] 
assert num_with_zero_final == num_with_zero_initial + num_rows_to_change, 
 'Number of rows needed to change not equal to number of rows changed.' 
df.to_csv('changed.csv')

pandas

share|improve this question

edited 2 days ago

Kiritee Gak

1,2991420

asked 2 days ago

elvin ugonnaelvin ugonna

11

New contributor

elvin ugonna is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

file_path = 'Processed_data/data1.csv' 
df = pd.read_csv(file_path) 
per_change = 0.1 
attr = 'Speed' 
target = 'Class' 
df_spd = df[df['Speed'] > 0.]

num_rows_to_change = int(df.shape[0] * per_change) 
num_with_zero_initial = df[df[attr] == 0].shape[0] 
assert df_spd.shape[0] > num_rows_to_change, 
 'Number of rows with non-zero speed is less than 10% of the original dataset.' 
df_update = df_spd.sample(num_rows_to_change) 
df_update[attr] = 0. 
df_update[target] = 0. 
df.update(df_update) 
update_list = df_update.index.tolist() 
num_with_zero_final = df[df['Speed'] == 0].shape[0] 
assert num_with_zero_final == num_with_zero_initial + num_rows_to_change, 
 'Number of rows needed to change not equal to number of rows changed.' 
df.to_csv('changed.csv')

share|improve this question

edited 2 days ago

Kiritee Gak

1,2991420

asked 2 days ago

elvin ugonnaelvin ugonna

11

New contributor

elvin ugonna is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

edited 2 days ago

Kiritee Gak

1,2991420

asked 2 days ago

elvin ugonnaelvin ugonna

11

New contributor

elvin ugonna is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

edited 2 days ago

Kiritee Gak

1,2991420

edited 2 days ago

Kiritee Gak

1,2991420

asked 2 days ago

elvin ugonnaelvin ugonna

11

New contributor

elvin ugonna is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 2 days ago

elvin ugonnaelvin ugonna

11

1 Answer
1

active

oldest

votes

0

$begingroup$

FYI, I did not go through your code as it is fairly straight forward assuming I had understood it right.

>> import pandas as pd
>> import random
>> df = pd.DataFrame('a': np.random.rand(10), b: np.random.rand(10))
>> print(df)
 a b
0 0.127409 0.508811
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.046671 0.592971
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296
>> idx = random.sample(df.index, int(len(df)*0.2)) # random indices from dataframe selection, 20% of them are selected based on 0.2
>> print(idx)
[6, 0]
>> df[df.index.isin(idx)] = [0, 0]
>> df
 a b
0 0.000000 0.000000
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.000000 0.000000
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296

Hope it helps.

share|improve this answer

answered 2 days ago

Kiritee GakKiritee Gak

1,2991420

$endgroup$

add a comment | 

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0

$begingroup$

FYI, I did not go through your code as it is fairly straight forward assuming I had understood it right.

>> import pandas as pd
>> import random
>> df = pd.DataFrame('a': np.random.rand(10), b: np.random.rand(10))
>> print(df)
 a b
0 0.127409 0.508811
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.046671 0.592971
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296
>> idx = random.sample(df.index, int(len(df)*0.2)) # random indices from dataframe selection, 20% of them are selected based on 0.2
>> print(idx)
[6, 0]
>> df[df.index.isin(idx)] = [0, 0]
>> df
 a b
0 0.000000 0.000000
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.000000 0.000000
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296

Hope it helps.

share|improve this answer

answered 2 days ago

Kiritee GakKiritee Gak

1,2991420

$endgroup$

add a comment | 

0

$begingroup$

FYI, I did not go through your code as it is fairly straight forward assuming I had understood it right.

>> import pandas as pd
>> import random
>> df = pd.DataFrame('a': np.random.rand(10), b: np.random.rand(10))
>> print(df)
 a b
0 0.127409 0.508811
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.046671 0.592971
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296
>> idx = random.sample(df.index, int(len(df)*0.2)) # random indices from dataframe selection, 20% of them are selected based on 0.2
>> print(idx)
[6, 0]
>> df[df.index.isin(idx)] = [0, 0]
>> df
 a b
0 0.000000 0.000000
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.000000 0.000000
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296

Hope it helps.

share|improve this answer

answered 2 days ago

Kiritee GakKiritee Gak

1,2991420

$endgroup$

add a comment | 

$begingroup$

FYI, I did not go through your code as it is fairly straight forward assuming I had understood it right.

>> import pandas as pd
>> import random
>> df = pd.DataFrame('a': np.random.rand(10), b: np.random.rand(10))
>> print(df)
 a b
0 0.127409 0.508811
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.046671 0.592971
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296
>> idx = random.sample(df.index, int(len(df)*0.2)) # random indices from dataframe selection, 20% of them are selected based on 0.2
>> print(idx)
[6, 0]
>> df[df.index.isin(idx)] = [0, 0]
>> df
 a b
0 0.000000 0.000000
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.000000 0.000000
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296

Hope it helps.

share|improve this answer

answered 2 days ago

Kiritee GakKiritee Gak

1,2991420

$endgroup$

>> import pandas as pd
>> import random
>> df = pd.DataFrame('a': np.random.rand(10), b: np.random.rand(10))
>> print(df)
 a b
0 0.127409 0.508811
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.046671 0.592971
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296
>> idx = random.sample(df.index, int(len(df)*0.2)) # random indices from dataframe selection, 20% of them are selected based on 0.2
>> print(idx)
[6, 0]
>> df[df.index.isin(idx)] = [0, 0]
>> df
 a b
0 0.000000 0.000000
1 0.345239 0.674797
2 0.824521 0.381567
3 0.893538 0.062142
4 0.307070 0.769546
5 0.872883 0.175192
6 0.000000 0.000000
7 0.799977 0.632761
8 0.932829 0.456906
9 0.188867 0.470296

share|improve this answer

answered 2 days ago

Kiritee GakKiritee Gak

1,2991420

answered 2 days ago

Kiritee GakKiritee Gak

1,2991420

answered 2 days ago

Kiritee GakKiritee Gak

1,2991420