Python Pandas convert column data type

Question

I know a question like this has been asked zillion types, but so far I have not been able to find an answer to this question.

I have joined two .csv files together with

Answer 1

As with @EdChum's comment, you need to use clip(upper=13) or clip_upper(13). One other option which can help you in the long run with instances like this is to use apply with a lambda function. This is a really nifty all-around method.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(5,18,size=(5, 4)), columns=list('ABCD'))
nscap = lambda x: min(x, 13)

print df.head()
print '-' * 20

df['NSCAP'] = df['D'].apply(nscap)

print df.head()

Result:

Take note of the last 2 lines of the second dataframe.

Hope this helps.

Answer 2

(Your code is missing a parenthesis at the end of nscap(df_joined["NS"].)

As @EdChum and @TheLaughingMan write, clip_upper is what you want here. This answer just addresses the direct reason for the error you're getting.

In the function

def nscap(ns):
    if ns <= 13:
        x = ns
    elif ns > 13:
        x = 13
    return x

effectively, ns <= 13 operations on a numpy.ndarray. When you compare such an array to a scalar, broadcasting takes place, and the result is an array where each element indicates whether it was true for it or not.

So

if ns <= 13:

translates to something like

if numpy.array([True, False, True, True]):

and it's impossible to understand whether this is true or not. That's the error you're getting: you need to specify whether you mean if all entries are true, if some entry is true, and so on.