Data Analytics With Python

 The find function is a great way to evaluate if a value is found within a text string. For example, I want to find all entries where email is missing "@" - I know it sounds very simple, but this is an example applicable to many different situations.  All you need to do is replace the "@" with a character of your choice:  import pandas as pd # create a sample DataFrame df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie'], 'Email': ['alice@example.com', 'bob@example.com', 'charlie@example.com'] }) # find the position of the '@' character in each email address df['Email Position'] = df['Email'].str.find('@') # display the updated DataFrame print(df) In this example, we first use the find() method to find the position of the '@' character in each email address in the 'Email' column. We then create a new column called 'Email Position...

There are often times when I first need to find where a specific piece of data lives and then use that information to take another action.   Suppose you have a DataFrame that contains a column called 'Address', which contains the full address of each person. You want to extract the zip code from each address, which appears after the last comma. You can use the rfind() method to find the index of the last comma in each string, and then extract the zip code using slicing. Here's an example code snippet that demonstrates how to use rfind() to extract the zip code from each address: import pandas as pd # create a sample DataFrame df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie'], 'Address': ['123 Main St, Anytown, USA 12345', '456 Oak Ave, Othertown, USA 67890', '789 Maple Dr, Another Town, USA 54321'] }) # extract the zip code from the 'Address' column using rfind() df['Zip Code'...

 One of the most powerful tools in the pandas library is the query() function, which allows you to filter and manipulate data in a very efficient way. The query() function is essentially a way of selecting a subset of data from a pandas DataFrame based on a set of criteria. You can think of it as similar to the WHERE clause in SQL, which allows you to select specific rows from a database based on certain conditions. What's interesting about the query() function is that it uses a special syntax that allows you to write these conditions in a way that is both more readable and more efficient than other methods of filtering data. Here's a simple example to illustrate how query() works. Let's say you have a DataFrame that contains information about some people, including their names, ages, and genders. You might use query() to select only the rows where the person's age is greater than 30 and their gender is female: import pandas as pd df = pd.DataFrame({ 'Name...