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Suppose I have a dataframe with countries that goes as:
cc | temp US | 37.0 CA | 12.0 US | 35.0 AU | 20.0 I know that there is a pd.get_dummies function to convert the countries to 'one-hot encodings'. However, I wish to convert them to indices instead such that I will get cc_index = [1,2,1,3] instead.
I'm assuming that there is a faster way than using the get_dummies along with a numpy where clause as shown below:
[np.where(x) for x in df.cc.get_dummies().values]
This is somewhat easier to do in R using 'factors' so I'm hoping pandas has something similar.
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First, to convert a Categorical column to its numerical codes, you can do this easier with: dataframe['c']. cat. codes . Further, it is possible to select automatically all columns with a certain dtype in a dataframe using select_dtypes .
We will be using . LabelEncoder() from sklearn library to convert categorical data to numerical data. We will use function fit_transform() in the process.
to_numeric() The best way to convert one or more columns of a DataFrame to numeric values is to use pandas. to_numeric(). This function will try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.
First, change the type of the column:
df.cc = pd.Categorical(df.cc) Now the data look similar but are stored categorically. To capture the category codes:
df['code'] = df.cc.cat.codes Now you have:
cc temp code 0 US 37.0 2 1 CA 12.0 1 2 US 35.0 2 3 AU 20.0 0 If you don't want to modify your DataFrame but simply get the codes:
df.cc.astype('category').cat.codes Or use the categorical column as an index:
df2 = pd.DataFrame(df.temp) df2.index = pd.CategoricalIndex(df.cc) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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