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In the excel sheet , i have two columns with large numbers.
But when i read the excel file with read_excel() and display the dataframe,
those two columns are printed in scientific format with exponential.
How can get rid of this format?
Thanks
Output in Pandas
364
Summary: Use the string literal syntax f"{number:. nf}" to suppress the scientific notation of a number to its floating-point representation.
Speed - Pandas is much faster than Excel, which is especially noticeable when working with larger quantities of data. Automation - A lot of the tasks that can be achieved with Pandas are extremely easy to automate, reducing the amount of tedious and repetitive tasks that need to be performed daily.
You can use the following basic syntax to convert a pandas DataFrame from a wide format to a long format: df = pd. melt(df, id_vars='col1', value_vars=['col2', 'col3', ...]) In this scenario, col1 is the column we use as an identifier and col2, col3, etc.
The way scientific notation is applied is controled via pandas' display options:
pd.set_option('display.float_format', '{:.2f}'.format) df = pd.DataFrame({'Traded Value':[67867869890077.96,78973434444543.44], 'Deals':[789797, 789878]}) print(df) Traded Value Deals 0 67867869890077.96 789797 1 78973434444543.44 789878 If this is simply for presentational purposes, you may convert your data to strings while formatting them on a column-by-column basis:
df = pd.DataFrame({'Traded Value':[67867869890077.96,78973434444543.44], 'Deals':[789797, 789878]}) df Deals Traded Value 0 789797 6.786787e+13 1 789878 7.897343e+13 df['Deals'] = df['Deals'].apply(lambda x: '{:d}'.format(x)) df['Traded Value'] = df['Traded Value'].apply(lambda x: '{:.2f}'.format(x)) df Deals Traded Value 0 789797 67867869890077.96 1 789878 78973434444543.44 An alternative more straightforward method would to put the following line at the top of your code that would format floats only:
pd.options.display.float_format = '{:.2f}'.format If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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