Looking at Types

1. To see the types of each column of a DataFrame, we can use print(df.dtypes)

2. The data types Pandas uses are:

   1. float

   2. int

   3. bool

   4. datetime

   5. timedelta

   6. category

   7. object

3. We can use what we know of regex to get rid of dollar signs:

   1. fruit.prices = fruit[‘price’].replace(‘[\$,]’,’’,regex=True)

4. We can use the Pandas function .to_nameric( ) to convert strings containing numerical values to integers or floats:

   1. fruit.price = pd.to_numeric(fruit.price)

5. To extract the numbers from a string, we can use Pandas .str.split( ) function:

   1. split_df = df[‘exerciseDescription’].str_split(‘(\dt)’, expand = True)