![]() ![]() When data is a bit complex to convert, we can create a custom function and apply it to each value to convert to the appropriate data type.įor instance, the money_col column, here is a simple function we can use: > def convert_money(value): value = value.replace('£','').replace(',', '') return float(value) > df. Creating a custom function to convert data to numbers The difference between this and above is that this method does the converting during the reading process and can be time-saving and more memory efficient. The dtype argument takes a dictionary with the key representing the column and the value representing the data type. Creating a custom function to convert data typeįor demonstration, we create a dataset and will load it with a function: import pandas as pd import numpy as np def load_df(): return pd.DataFrame( ).Defining data types when reading a CSV file. ![]() Converting multiple data columns at once.Converting a column of mixed data types.More specifically, you will learn how to use the Pandas built-in methods astype() and to_numeric() to deal with the following common problems: This article will discuss how to change data to a numeric type. In the case of Pandas, it will correctly infer data types in many cases and you can move on with your analysis without any further thought on the topic.ĭespite how well pandas works, at some point in your data analysis process you will likely need to explicitly convert data from one type to another. Otherwise, you may get unexpected results or errors. When doing data analysis, it is important to ensure correct data types. ![]()
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