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The types of data manipulation required depend on your business requirements. In this first recipe, we saw a few ways to carry out data manipulation, but there is no limit to what you can do and how you can manipulate data for analysis. 

We have also seen how to convert a numerical variable into a categorical variable. We can do this kind of typecasting in many ways. For example, we can convert a categorical variable into a numerical variable, if required, with the following code:

# Converting a categorical variable to numerical
# Using astype() to cast a pandas object to a specified datatype

# Here we typecast GarageYrBlt from float64 type to int64 type
housepricesdata['GarageYrBlt'] = housepricesdata['GarageYrBlt'].astype('int64')

You can only convert the GarageYrBlt variable if it does not contain any missing values. The preceding code will throw an error, since GarageYrBlt contains missing values. 

We have looked at how we can use one-hot encoding to convert categorical variables to numerical variables, and why we do this. In addition to one-hot encoding, we can perform other kinds of encoding, such as label encoding, frequency encoding, and so on. An example code for label encoding is given in the following code block:

# We use sklearn.preprocessing and import LabelEncoder class
from sklearn.preprocessing import LabelEncoder

# Create instance of LabelEncoder class
lb_make = LabelEncoder()

# We create a new variable LotConfig_code to hold the new numerical labels
# We label encode LotConfig variable
housepricesdata["LotConfig_Code"] = lb_make.fit_transform(housepricesdata["LotConfig"])

# Display the LotConfig variable and its corresponding label encoded numerical values
housepricesdata[["LotConfig", "LotConfig_Code"]]