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I have a column called accountnumber with values similar to 4.11889000e+11 in a pandas dataframe. I want to suppress the scientific notation and convert the values to 4118890000. I have tried the following method and did not work.
df = pd.read_csv(data.csv)
pd.options.display.float_format = '{:,.3f}'.format
Please recommend.
252
Use a string literal to suppress scientific notation Use the string literal syntax f"{num:. nf}" to represent num in decimal format with n places following the decimal point.
Within a given f-string, you can use the {...:f} format specifier to tell Python to use floating point notation for the number preceding the :f suffix. Thus, to print the number my_float = 0.00001 non-scientifically, use the expression print(f'{my_float:f}') .
There is no direct way to configure and stop scientific notation in spark however you can apply format_number function to display number in proper decimal format rather than exponential format.
I assume the exponential notation for the account numbers must come from the data file. If I create a small csv with the full account numbers, pandas will interpret them as integers.
acct_num
0 4118890000
1 9876543210
df['acct_num'].dtype
Out[51]: dtype('int64')
However, if the account numbers in the csv are represented in exponential notation then pandas will read them as floats.
acct_num
0 4.118890e+11
1 9.876543e+11
df['acct_num'].dtype
Out[54]: dtype('float64')
You have 2 options. First, correct the process that creates the csv so the account numbers are written out correctly. The second is to change the data type of the acct_num column to integer.
df['acct_num'] = df['acct_num'].astype('int64')
df
Out[66]:
acct_num
0 411889000000
1 987654321000
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