Reputation: 87
I have a pandas dataframe with more than 50 columns. All the data except the 1st column is float. I want to replace any value greater than 5.75 with 100. Can someone advise any function to do the same.
The replace function is not working as to_value can only take "=" function, and not the greater than function.
Upvotes: 1
Views: 1159
Reputation: 447
This can be done using
df['ColumnName'] = np.where(df['ColumnName'] > 5.75, 100, df['First Season'])
Upvotes: 1
Reputation: 961
import numpy as np
import pandas as pd
#Create df
np.random.seed(0)
df = pd.DataFrame(2*np.random.randn(100,50))
for col_name in df.columns[1:]: #Skip first column
df.loc[:,col_name][df.loc[:,col_name] > 5.75] = 100
Upvotes: 0
Reputation: 2114
If you have a dataframe:
import pandas as pd
import random
df = pd.DataFrame({'first_column': [random.uniform(5,6) for x in range(10)]})
print(df)
Gives me:
first_column
0 5.620439
1 5.640604
2 5.286608
3 5.642898
4 5.742910
5 5.096862
6 5.360492
7 5.923234
8 5.489964
9 5.127154
Then check if the value is greater than 5.75:
df[df > 5.75] = 100
print(df)
Gives me:
first_column
0 5.620439
1 5.640604
2 5.286608
3 5.642898
4 5.742910
5 5.096862
6 5.360492
7 100.000000
8 5.489964
9 5.127154
Upvotes: 0
Reputation: 10789
You can make a custom function and pass it to apply:
import pandas as pd
import random
df = pd.DataFrame({'col_name': [random.randint(0,10) for x in range(100)]})
def f(x):
if x >= 5.75:
return 100
return x
df['modified'] = df['col_name'].apply(f)
print(df.head())
col_name modified
0 2 2
1 5 5
2 7 100
3 1 1
4 9 100
Upvotes: 0