Changing the Data Type

You already know how to change the data type from string to number, for example. But let's take a closer look at this small but important task.

Let's start by changing the data type from string to datetime. Most often, you will need this to work with time series. You can perform this operation using the .to_datetime() method:

To convert a string to a bool - use the .map() method on the column whose values you want to change:

For example, if you have a price column that looks like "$198,800" and you want to turn it into a float - you should create custom transformation functions:


              12345678910111213
            
import pandas as pd
import re

# Create simple dataset
df = pd.DataFrame(data={'Price':['$4,122.94', '$1,002.3']})

# Create a custom function to transform data
# x - value from column
def price2int(x):
  return float(re.sub(r'[\$\,]', '', x))

# Use custom transformation on a column
df['Price'] = df['Price'].apply(price2int)

Tâche

Swipe to start coding

Read the sales_data_types.csv dataset and change the data type in the Active column from str to bool.

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 1. Chapitre 5

Changing the Data Type

You already know how to change the data type from string to number, for example. But let's take a closer look at this small but important task.

Let's start by changing the data type from string to datetime. Most often, you will need this to work with time series. You can perform this operation using the .to_datetime() method:

To convert a string to a bool - use the .map() method on the column whose values you want to change:

For example, if you have a price column that looks like "$198,800" and you want to turn it into a float - you should create custom transformation functions:


              12345678910111213
            
import pandas as pd
import re

# Create simple dataset
df = pd.DataFrame(data={'Price':['$4,122.94', '$1,002.3']})

# Create a custom function to transform data
# x - value from column
def price2int(x):
  return float(re.sub(r'[\$\,]', '', x))

# Use custom transformation on a column
df['Price'] = df['Price'].apply(price2int)

Tâche

Swipe to start coding

Read the sales_data_types.csv dataset and change the data type in the Active column from str to bool.

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 1. Chapitre 5

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous