Output data Formatting - Decimal place, Missing values

[mitalee.ghy]

I am using pandas DataFrame in my app. I am using following code to display the data.

The output have too many decimal places and display “NaN” or “nan” if data is missing. Out put are showing below.

Please suggest how to resolve the following problem:

  * Customize number of decimal places
  * Remove or Replace "NaN" or "nan" if data is missing. 

Thank you in advance.

[Charly_Wargnier]

Hi @mitalee.ghy, and welcome to the community!

choosing ‘N’ decimals

You can call str.format(float) with str as "{:.xf}" to get a string representation of float with x decimal places.

value = 3.3333333333
formatted_string = "{:.2f}".format(value)
# format to two decimal places
float_value = float(formatted_string)
st.write(float_value)

choosing ‘N’ decimals right in Pandas

You can also do this directly in Pandas!

By providing an integer each column is rounded to the same number of decimal places, e.g.:

df.round(1)

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value, e.g.:

df.round({'dogs': 1, 'cats': 0})

NaN values

You can replace NaN values with null via df.fillna(’’, inplace=True)

I hope this solves your issue. Let us know

Best,
Charly

[rpontual]

For simplicity and I suggest the single line formatting below

st.write(’- Monthly Sales: ', df_mar2021[‘MonSales’].style.format("{:.2f}")

Note: This requires Pandas 1.2.4, there is a regression related to 1.3.0 that is not yet resolved.