How to identify strings in my Pandas dataframe
Let’s say I have a dataframe like this:
Pandas how to Identify strings in my dataframe
Let’s say I have a dataframe like this:
creating a multi-index column from an existing dataframe
i have data that is coming to me as it appears in a spreadsheet.
WHen it arrives it looks like this:
The np.where filter does not equal 0, but ‘ZeroDivisionError: float division by zero’ appears
In my code, I use the following calculation for a column in the dataframe:
np.where(df_score['number'] ! = 0, 100 - ((100 * df_score[rank_column] -50)/df_score['number']), None),I have used df_score['number']! = 0, but the code is still wrong, ZeroDivisionError: float division by zero, even if I put df_score['number']! = 0 changed to df_score['number'] > 0, why?
Fill nan based on other rows/columns and other dataframe
I have the following “covar” dataframe (it’s a covariance matrix), where I have the same items both as index and as column names.
How to Calcualte GDP average?
def average_gdp(): Top15[‘avgGDP’] = Top15[[str(year) for year in range(2006, 2016)]].apply(lambda x: x.mean(), axis=1) avgGDP = Top15[‘avgGDP’].sort_values(ascending=False) sixth_largest = avgGDP.iloc[5] return (sixth_largest) From Snap code above I do like traditional one like this but. Seem not what is the purpose to create in NumPy and Pandas, How to improve this code. I would like to create […]