Why is conditional clause comparing dataframe columns causing error even though conditional clause is boolean in nature?

Question

I am trying to build logic that compares two columns in a dataframe after some logic has been verified.

Here is my code, it is pulling historical rates for cryptocurrency data from gdax.com. The test condition I am applying is 'if df.column 4 is greater than sum of df.column4 and df.column3 then buy 10% of account'

import GDAX
import pandas as pd
import numpy as np

public_client = GDAX.PublicClient()
ticker_call = public_client.getProductHistoricRates(product='LTC-USD')

df = pd.DataFrame(ticker_call)


df['account']=100



if df[4] > df[3] + df[4]:
    df['account_new'] = df['account']-df['account'] *.10
    print df

I am getting the error 'ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().' for the if statement, what I dont understand is why? when I run each of the lines in the if statement individually they work. How can I fix this issue?

Why and how do I use a.bool() instead of an if statement?

thank you in advance.

Pietro Tortella · Accepted Answer · 2017-07-04 14:52:40Z

1

df[4] > df[3] + df[4]

(that actually is equivalent to df[3] < 0) is a pandas.Series of boolean values. When do you want to enter the if statement? When all values are True? then you should use all(). When any of them is True? Then you should use any().

If instead you want to execute that function for any row when the condition is True, you should do something like

condition = df[4] > df[3] + df[4]
true_df = df[condition]
true_df["account_new"] = true_df['account']-true_df['account'] *.10

but now the columns "account_new" exists only in true_df, and not in df.

With something like

df["account_new"] = true_df["account_new"]

now also df has the column "account_new", but in the lines where condition is false you have nans...

answered Jul 4, 2017 at 14:52

Pietro Tortella

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3 Comments

RustyShackleford Over a year ago

Hey @PietroTortella, that worked beautifully and you understood correct when all values are true is when I want to enter the if statement. Is it better to create conditional statements in pandas without the if statement and more so the way you made it?

Pietro Tortella Over a year ago

It depends on the use you want to do... in your case doing if all(condition): is fine, in some other cases you'll need to perform an operation on the rows of a dataframe satisfying a condition, and you'll use the second strategy...

RustyShackleford Over a year ago

can chain multiple if-else statements under one all() clause? for example if all(condition): elseif(condtion):

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