Using Apply In Pandas Lambda Functions With Multiple If Statements

I'm trying to infer a classification according to the size of a person in a dataframe like this one: Size 1 80000 2 8000000 3 8000000000 ... I want it to look li

Solution 1:

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 valuedeffunc(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
080000            <1m
180000001-10m
2800000000            N/A

Solution 2:

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)

Solution 3:

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 copyof df withnewcolumn
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.

Solution 4:

The apply lambda function actually does the job here, I just wonder what the problem was.... as your syntax looks ok and it works....

df1= [80000, 8000000, 8000000000, 800000000000]
df=pd.DataFrame(df1)
df.columns=['size']
df['Classification']=df['size'].apply(lambda x: '<1m'if x<1000000  else'1-10m'if 1000000<x<10000000 else'1bi')
df

Output: