vectorize conditional assignment in pandas dataframe

Question

If I have a dataframe df with column x and want to create column y based on values of x using this in pseudo code:

if df['x'] < -2 then df['y'] = 1 
else if df['x'] > 2 then df['y'] = -1 
else df['y'] = 0

How would I achieve this? I assume np.where is the best way to do this but not sure how to code it correctly.

Asked By: azuric

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Source

Answer 1

One simple method would be to assign the default value first and then perform 2 loc calls:

In [66]:

df = pd.DataFrame({'x':[0,-3,5,-1,1]})
df
Out[66]:
   x
0  0
1 -3
2  5
3 -1
4  1

In [69]:

df['y'] = 0
df.loc[df['x'] < -2, 'y'] = 1
df.loc[df['x'] > 2, 'y'] = -1
df
Out[69]:
   x  y
0  0  0
1 -3  1
2  5 -1
3 -1  0
4  1  0

If you wanted to use np.where then you could do it with a nested np.where:

In [77]:

df['y'] = np.where(df['x'] < -2 , 1, np.where(df['x'] > 2, -1, 0))
df
Out[77]:
   x  y
0  0  0
1 -3  1
2  5 -1
3 -1  0
4  1  0

So here we define the first condition as where x is less than -2, return 1, then we have another np.where which tests the other condition where x is greater than 2 and returns -1, otherwise return 0

timings

In [79]:

%timeit df['y'] = np.where(df['x'] < -2 , 1, np.where(df['x'] > 2, -1, 0))

1000 loops, best of 3: 1.79 ms per loop

In [81]:

%%timeit
df['y'] = 0
df.loc[df['x'] < -2, 'y'] = 1
df.loc[df['x'] > 2, 'y'] = -1

100 loops, best of 3: 3.27 ms per loop

So for this sample dataset the np.where method is twice as fast

Answered By: EdChum

Answer 2

This is a good use case for pd.cut where you define ranges and based on those ranges you can assign labels:

df['y'] = pd.cut(df['x'], [-np.inf, -2, 2, np.inf], labels=[1, 0, -1], right=False)

Output

Answered By: Erfan

Answer 3

Use `np.select` for multiple conditions

np.select(condlist, choicelist, default=0)

Return elements in choicelist depending on the corresponding condition in condlist.

The default element is used when all conditions evaluate to False.

condlist = [
    df['x'] < -2,
    df['x'] > 2,
]
choicelist = [
    1,
    -1,
]
df['y'] = np.select(condlist, choicelist, default=0)

np.select is much more readable than a nested np.where but just as fast:

^{df = pd.DataFrame({'x': np.random.randint(-5, 5, size=n)})}

Answered By: tdy

Answer 4

You can do it easily using the index and 2 loc calls:

df = pd.DataFrame({'x':[0,-3,5,-1,1]})

df

   x
0  0
1 -3
2  5
3 -1
4  1
    
df['y'] = 0
idx_1 = df.loc[df['x'] < -2, 'y'].index
idx_2 = df.loc[df['x'] >  2, 'y'].index
df.loc[idx_1, 'y'] =  1
df.loc[idx_2, 'y'] = -1

df

   x  y
0  0  0
1 -3  1
2  5 -1
3 -1  0
4  1  0

Answered By: Alexander Martins

vectorize conditional assignment in pandas dataframe

Question:

Answers:

Use `np.select` for multiple conditions

vectorize conditional assignment in pandas dataframe

Question:

Answers:

Use np.select for multiple conditions

Use `np.select` for multiple conditions