CODEWITHSUNDEEP
pythonpandasnumpynumpy-ndarray
daveskis
daveskis

Reputation: 153

Python convert conditional array to dataframe

I have a dataframe with the following information:

    ticker       date      close       gap
0      BHP 1981-07-31   0.945416 -0.199458
1      BHP 1981-08-31   0.919463 -0.235930
2      BHP 1981-09-30   0.760040 -0.434985
3      BHP 1981-10-30   0.711842 -0.509136
4      BHP 1981-11-30   0.778578 -0.428161
..     ...        ...        ...       ...
460    BHP 2019-11-29  38.230000  0.472563
461    BHP 2019-12-31  38.920000  0.463312
462    BHP 2020-01-31  39.400000  0.459691
463    BHP 2020-02-28  33.600000  0.627567
464    BHP 2020-03-31  28.980000  0.784124

I developed the following code to find where the rows are when it crosses 0:

zero_crossings =np.where(np.diff(np.sign(BHP_data['gap'])))[0]

This returns:

array([ 52,  54,  57,  75,  79,  86,  93, 194, 220, 221, 234, 235, 236,
       238, 245, 248, 277, 379, 381, 382, 383, 391, 392, 393, 395, 396],
      dtype=int64)

I need to be able to do the following:

However, I don't know how to turn this nd.array into something useful that I can make the calculations from. When I try:

pd.DataFrame(zero_crossings)

I get the following df, which only returns the index:

      0
0    52
1    54
2    57
3    75
4    79
5    86
..   ..

Please help...

Upvotes: 1

Views: 64

Answers (1)

Kavin Dsouza
Kavin Dsouza

Reputation: 989

Just extended your code a bit to get the zero_crossings into the original dataframe as required.

import pandas as pd
import numpy as np

BHP_data = pd.DataFrame({'gap': [-0.199458, 0.472563, 0.463312, 0.493318, -0.509136, 0.534985, 0.784124]})
BHP_data['zero_crossings'] = 0

zero_crossings = np.where(np.diff(np.sign(BHP_data['gap'])))[0]
print(zero_crossings) # [0 3 4]

# Updates the column to 1 based on the 0 crossing
BHP_data.loc[zero_crossings, 'zero_crossings'] = 1

print(BHP_data)

Output

        gap  zero_crossings
0 -0.199458               1
1  0.472563               0
2  0.463312               0
3  0.493318               1
4 -0.509136               1
5  0.534985               0
6  0.784124               0

Upvotes: 2

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