I want to make a new column based on two variables. I want my new column to have the value "Good" if (column 1 >= .5 or column 2 < 0.5) and (column 1 < .5 or column 2 >= 0.5) otherwise "Bad".
I tried using lambda and if.
df["new column"] = df[["column 1", "column 2"]].apply(
lambda x, y: "Good" if (x >= 0.5 or y < 0.5) and (x < 0.5 or y >= 0.5) else "Bad"
)
Got
TypeError: ("() missing 1 required positional argument: 'y'", 'occurred at index column 1')
Use np.where, pandas does intrinsic data alignment, meaning you don't need to use apply or iterate row by row, pandas will align the data on index:
df['new column'] = df['new column'] = np.where(((df['y'] <= .5) | (df['x'] > .5)) & ((df['x'] < .5) | (df['y'] >= .5)), 'Good', 'Bad')
df
Using @YunaA. setup....
import pandas as pd
df = pd.DataFrame({'x': [1, 2, 0.1, 0.1],
'y': [1, 2, 0.7, 0.2],
'column 3': [1, 2, 3, 4]})
df['new column'] = df['new column'] = np.where(((df['y'] <= .5) | (df['x'] > .5)) & ((df['x'] < .5) | (df['y'] >= .5)), 'Good', 'Bad')
df
Output:
x y column 3 new column
0 1.0 1.0 1 Good
1 2.0 2.0 2 Good
2 0.1 0.7 3 Bad
3 0.1 0.2 4 Good
import pandas as pd
import numpy as np
np.random.seed(123)
df = pd.DataFrame({'x':np.random.random(100)*2,
'y': np.random.random(100)*1})
def update_column(row):
if (row['x'] >= .5 or row['y'] <= .5) and (row['x'] < .5 or row['y'] >= .5):
return "Good"
return "Bad"
Results
%timeit df['new column'] = np.where(((df['y'] <= .5) | (df['x'] > .5))
& ((df['x'] < .5) | (df['y'] >= .5)), 'Good', 'Bad')
1.45 ms ± 72.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit df['new_column'] = df.apply(update_column, axis=1)
5.83 ms ± 484 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Try this:
import pandas as pd
def update_column(row):
if (row['x'] >= .5 or row['y'] <= .5) and (row['x'] < .5 or row['y'] >= .5):
return "Good"
return "Bad"
df['new_column'] = df.apply(update_column, axis=1)
Pass the row into the lambda instead.
df['new column'] = df[['column 1', 'column 2']].apply(lambda row: "Good" if (row['column 1'] >= .5 or row['column 2'] < .5) and (row['column 1'] < .5 or row['column 2'] >= .5) else "Bad", axis=1)
Example:
import pandas as pd
df = pd.DataFrame({'column 1': [1, 2, 0.1, 0.1],
'column 2': [1, 2, 0.7, 0.2],
'column 3': [1, 2, 3, 4]})
df['new column'] = df[['column 1', 'column 2']].apply(lambda row: "Good" if (row['column 1'] >= .5 or row['column 2'] < .5) and (row['column 1'] < .5 or row['column 2'] >= .5) else "Bad", axis=1)
print(df)
Output:
column 1 column 2 column 3 new column
0 1.0 1.0 1 Good
1 2.0 2.0 2 Good
2 0.1 0.7 3 Bad
3 0.1 0.2 4 Good
You just need to reference the columns by their index in the array you are passing the the lambda expression, like this:
df["new column"] = df[["column 1", "column 2"]].apply(
lambda x: "Good" if (x[0] >= 0.5 or x[1] < 0.5) and (x[0] < 0.5 or x[1] >= 0.5) else "Bad", axis=1
)
NOTE: don't forget to include axis=1
