Issue with creating a column using np.where, ArrayType error

Question

I have a dataframe in which I'm trying to create a binary 1/0 column when certain conditions are met. The code I'm using is as follows:

sd_threshold = 5

df1["signal"] = np.where(np.logical_and(df1["high"] >= df1["break"], df1["low"] 
<= df1["break"], df1["sd_round"] > sd_threshold), 1, 0)

The code returns TypeError: return arrays must be of ArrayType when the last condition df1["sd_round"] > sd_threshold is included, otherwise it works fine. There isn't any issue with the data in the df1["sd_round"] column.

Any insight would be much appreciated, thank you!

apteryx · Accepted Answer · 2018-08-07 17:09:24Z

2

check the documentation -- np.logical_and() compares the first two arguments you give it and writes the output to the third. you could use a nested call but i would just go with & (pandas boolean indexing):

df1["signal"] = np.where((df1["high"] >= df1["break"]) & 
                         (df1["low"] <= df1["break"]) &
                         (df1["sd_round"] > sd_threshold), 
                         1, 0)

EDIT: you could actually just skip numpy and cast your boolean Series to int to yield 1s and 0s:

mask = ((df1["high"] >= df1["break"]) & 
        (df1["low"] <= df1["break"]) &
        (df1["sd_round"] > sd_threshold))
df1["signal"] = mask.astype(int)

answered Aug 7, 2018 at 17:09

apteryx

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