 # Data Cleaning RMOTR - FreeCodeCamp

Last updated: September 3rd, 2020  # Missing Data¶ ## Hands on!¶

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import numpy as np
import pandas as pd


What does "missing data" mean? What is a missing value? It depends on the origin of the data and the context it was generated. For example, for a survey, a Salary field with an empty value, or a number 0, or an invalid value (a string for example) can be considered "missing data". These concepts are related to the values that Python will consider "Falsy":

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falsy_values = (0, False, None, '', [], {})


For Python, all the values above are considered "falsy":

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any(falsy_values)


Numpy has a special "nullable" value for numbers which is np.nan. It's NaN: "Not a number"

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np.nan


The np.nan value is kind of a virus. Everything that it touches becomes np.nan:

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3 + np.nan

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a = np.array([1, 2, 3, np.nan, np.nan, 4])

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a.sum()

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a.mean()


This is better than regular None values, which in the previous examples would have raised an exception:

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3 + None


For a numeric array, the None value is replaced by np.nan:

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a = np.array([1, 2, 3, np.nan, None, 4], dtype='float')

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a


As we said, np.nan is like a virus. If you have any nan value in an array and you try to perform an operation on it, you'll get unexpected results:

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a = np.array([1, 2, 3, np.nan, np.nan, 4])

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a.mean()

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a.sum()


Numpy also supports an "Infinite" type:

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np.inf


Which also behaves as a virus:

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3 + np.inf

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np.inf / 3

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np.inf / np.inf

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b = np.array([1, 2, 3, np.inf, np.nan, 4], dtype=np.float)

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b.sum() ### Checking for nan or inf¶

There are two functions: np.isnan and np.isinf that will perform the desired checks:

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np.isnan(np.nan)

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np.isinf(np.inf)


And the joint operation can be performed with np.isfinite.

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np.isfinite(np.nan), np.isfinite(np.inf)


np.isnan and np.isinf also take arrays as inputs, and return boolean arrays as results:

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np.isnan(np.array([1, 2, 3, np.nan, np.inf, 4]))

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np.isinf(np.array([1, 2, 3, np.nan, np.inf, 4]))

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np.isfinite(np.array([1, 2, 3, np.nan, np.inf, 4]))


Note: It's not so common to find infinite values. From now on, we'll keep working with only np.nan ### Filtering them out¶

Whenever you're trying to perform an operation with a Numpy array and you know there might be missing values, you'll need to filter them out before proceeding, to avoid nan propagation. We'll use a combination of the previous np.isnan + boolean arrays for this purpose:

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a = np.array([1, 2, 3, np.nan, np.nan, 4])

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a[~np.isnan(a)]


Which is equivalent to:

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a[np.isfinite(a)]


And with that result, all the operation can be now performed:

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a[np.isfinite(a)].sum()

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a[np.isfinite(a)].mean() 