Python one-line boolean
Question:
Given df
col1
0 -12
1 -64
2 24
3 2
4 6
5 75
6 3
7 10
8 42
9 15
10 14
I’m trying to make something like:
df_new
col1
0 0
1 0
2 1
3 0
4 0
5 1
6 0
7 0
8 1
9 0
10 0
Where, if the number is greater than 15, then it is 1 and 0 otherwise. I tried to do it this way:
df_new = [1 for x in df if (x<15) else 0]
But it gives a syntax error.
Answers:
You can apply the condition to the entire df all at once:
new_df = df > 15
The result will be a boolean, but will behave as zeros and ones in all the ways that you care about.
As for your original expression, a ternary operator has the form [a if cond else b for x in iterable] while a filter looks like [a for x in iterable if cond]. Notice where the if goes in both cases.
You can convert the booleans to ints like this:
df_new = (df>15).astype(int)
While others are right about using the features specific to dataframes, your understanding of list comprehension syntax is only a little bit off.
[1 if x < 15 else 0 for x in df]
Or if parentheses help you to see what’s going on:
[(1 if x < 15 else 0) for x in df]
Simply use where function.
import pandas as pd
import numpy as np
df=pd.DataFrame([-12,-64,24,2,6,75,3,10,42,15,14])
df=np.where(df>15,1,0)
df=pd.DataFrame(df)
Syntax : numpy.where(condition[, x, y])
Given df
col1
0 -12
1 -64
2 24
3 2
4 6
5 75
6 3
7 10
8 42
9 15
10 14
I’m trying to make something like:
df_new
col1
0 0
1 0
2 1
3 0
4 0
5 1
6 0
7 0
8 1
9 0
10 0
Where, if the number is greater than 15, then it is 1 and 0 otherwise. I tried to do it this way:
df_new = [1 for x in df if (x<15) else 0]
But it gives a syntax error.
You can apply the condition to the entire df all at once:
new_df = df > 15
The result will be a boolean, but will behave as zeros and ones in all the ways that you care about.
As for your original expression, a ternary operator has the form [a if cond else b for x in iterable] while a filter looks like [a for x in iterable if cond]. Notice where the if goes in both cases.
You can convert the booleans to ints like this:
df_new = (df>15).astype(int)
While others are right about using the features specific to dataframes, your understanding of list comprehension syntax is only a little bit off.
[1 if x < 15 else 0 for x in df]
Or if parentheses help you to see what’s going on:
[(1 if x < 15 else 0) for x in df]
Simply use where function.
import pandas as pd
import numpy as np
df=pd.DataFrame([-12,-64,24,2,6,75,3,10,42,15,14])
df=np.where(df>15,1,0)
df=pd.DataFrame(df)
Syntax : numpy.where(condition[, x, y])