Using Apply in Pandas Lambda functions with multiple if statements
Here is a small example that you can build upon:
Basically, lambda x: x..
is the short one-liner of a function. What apply really asks for is a function which you can easily recreate yourself.
import pandas as pd
# Recreate the dataframe
data = dict(Size=[80000,8000000,800000000])
df = pd.DataFrame(data)
# Create a function that returns desired values
# You only need to check upper bound as the next elif-statement will catch the value
def func(x):
if x < 1e6:
return "<1m"
elif x < 1e7:
return "1-10m"
elif x < 5e7:
return "10-50m"
else:
return 'N/A'
# Add elif statements....
df['Classification'] = df['Size'].apply(func)
print(df)
Returns:
Size Classification
0 80000 <1m
1 8000000 1-10m
2 800000000 N/A
You can use pd.cut
function:
bins = [0, 1000000, 10000000, 50000000, ...]
labels = ['<1m','1-10m','10-50m', ...]
df['Classification'] = pd.cut(df['Size'], bins=bins, labels=labels)
Using Numpy's searchsorted
labels = np.array(['<1m', '1-10m', '10-50m', '>50m'])
bins = np.array([1E6, 1E7, 5E7])
# Using assign is my preference as it produces a copy of df with new column
df.assign(Classification=labels[bins.searchsorted(df['Size'].values)])
If you wanted to produce new column in existing dataframe
df['Classification'] = labels[bins.searchsorted(df['Size'].values)]
Some Explanation
From Docs:np.searchsorted
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array a such that, if the corresponding elements in v were inserted before the indices, the order of a would be preserved.
The labels
array has a length greater than that of bins
by one. Because when something is greater than the maximum value in bins
, searchsorted
returns a -1
. When we slice labels
this grabs the last label.