Thank you 😊 So to Sum up, we can filter data in pandas through: - Boolean Mask, - loc (and unnecessarily iloc), - query(), - lambda, - isnull, and which you discussed in detail in the next video, Thx body 🙂 🙂
In the lambda df{df.apply(lambda x :x["City"] =="Houston" and x["Category"] == "Technology", axis = 1)] how can I add one more value to Category (x["Category"] == "Technology" and "Office Supplies, axis = 1] for example? Similar that the example that you show on excel filtering multiple values in two different columns
I have multiple conditions to validate. if ‘Country’ column has specific value and ‘Age’ column is less than 11 or ‘DOB’ column has date after 2010 or ‘Comments’ column contains few different strings from a list and create a new column that prints value ‘Valid’ if all conditions meet else prints ‘Invalid’. Need to define a function and apply to multiple data frames. I’m so lost
Thanks for you comment. First of all try to apply filter on country , age and DOB column right, it is easy, and I explained in the video, after that you can go for other condition with the same logic.
Hi, if the date is stored in string format then try to convert using pd.to_datetime or you can use datetime index and strptime method to convert string in date time object
@@mcquezchima6402 Thanks. We can apply filter by couple of ways: 1.) df1["Order Date"] = pd.to_datetime(df['Order Date']) df[df1['Order Date'].dt.year == 2008] 2.) df[df['Order Date'].str.contains('2008')] Try and let me know.
