Two very useful Python libraries were used in Pandas for numeric conversion.
CONVERT STRING TO NUMBER PYTHON WITHOUT CASTING HOW TO
It was shown how to map values to Python mathematical expressions and how to evaluate them. We covered the reverse case of conversion of large numbers to abbreviations. In this post, we saw how to convert human readable and language expressions to numbers. So the Pandas code to convert numbers is: from numerizer import numerizeīelow we can find a table of large number abbreviations which can be used to improve the mapping. This time we will use Python library: numerize - which can be installed by: pip install numerize Step 3: Convert words to number - one to 1įinally let's cover the case when we need to convert language numerics into numbers: Library can be installed by: pip install humanize The library supports multiple languages - about 25 like: The result contains the converted values: 0 110 It will help us to** convert easily and reliably large numbers to human readable abbreviations**: import humanize It works well also for numeric values with spaces like 1 M Step 2: Convert Thousand/Million to K/Mįor this step we will use the Python library - humanize. This will give us: array(, dtype=object)Īs we can see it works fine for columns with mixed data. Pd.eval(df.replace(mp.keys(), mp.values(), regex=True)) We will map the abbreviations to the math expression. 'lang': }Ĭolumns=)įirst we will start the conversion of large number abbreviations to numbers. To use positional formatting, use the 's' prefix in front of the number, and Python will do the rest. Let's use the following DataFrame the conversion from natural language numerals to numbers: import pandas as pd Positional formatting is one of the oldest methods in Python to convert an integer into a string. numerizer - convert natural language numerics into ints and floats.humanize - turning a number into a fuzzy human-readable expression.
We will also cover the reverse case converting thousand and million to K and M. We will show two different ways for conversion of K and M to thousand and million. In this short tutorial, we'll cover how to convert natural language numerics like M and K into numbers with Pandas and Python.
0 kommentar(er)
