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import numpy as np
import pandas as pd
import tia.bbg.datamgr as dm
mgr = dm.BbgDataManager()
bb_yearb4 = "2016-12-30"
bb_today = "2017-09-22"
indices = [list of indices]
sids_index = mgr[indices]
df_idx = sids_index.get_historical('PX_LAST', bb_yearb4, bb_today)
nan = np.nan
price_test = {}
for index in indices:
price_test["{0}".format(index)] = df_idx.loc[bb_today][index]
The output shows multiple nan float values:
In [1]: price_test.values()
Out[1]: [nan, nan, nan, 47913.199999999997, nan, 1210.3299999999999, nan]
However, testing for nan shows false:
In [2]: nan in price_test.values()
Out[2]: False
What is the correct way to test this?
940
To check whether a floating point or double number is NaN (Not a Number) in C++, we can use the isnan() function. The isnan() function is present into the cmath library. This function is introduced in C++ version 11.
nan . In Working with missing data, we saw that pandas primarily uses NaN to represent missing data. Because NaN is a float, this forces an array of integers with any missing values to become floating point.
isNaN() Method: To determine whether a number is NaN, we can use the isNaN() function. It is a boolean function that returns true if a number is NaN otherwise returns false.
NaN is weird, because NaN != NaN. There's a good reason for that, but it still breaks in checks and everything else that assumes normal == behavior.
Check for NaN with NaN-specific checks, like numpy.isnan:
any(np.isnan(val) for val in d.values())
or in a non-NumPy context,
any(math.isnan(val) for val in d.values())
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