pandas - How do I print entire number in Python from describe() function?

Question

I am doing some statistical work using Python's pandas and I am having the following code to print out the data description (mean, count, median, etc).

data=pandas.read_csv(input_file)
print(data.describe())

But my data is pretty big (around 4 million rows) and each rows has very small data. So inevitably, the count would be big and the mean would be pretty small and thus Python print it like this.

I just want to print these numbers entirely just for ease of use and understanding, for example it better be 4393476 instead of 4.393476e+06. I have googled it around and the most I can find is Display a float with two decimal places in Python and some other similar posts. But that will only work only if I have the numbers in a variable already. Not in my case though. In my case I haven't got those numbers. The numbers are created by the describe() function, so I don't know what numbers I will get.

Sorry if this seems like a very basic question, I am still new to Python. Any response is appreaciated. Thanks.

Answer 1

Suppose you have the following DataFrame:

Edit

I checked the docs and you should probably use the pandas.set_option API to do this:

In [13]: df
Out[13]: 
              a             b             c
0  4.405544e+08  1.425305e+08  6.387200e+08
1  8.792502e+08  7.135909e+08  4.652605e+07
2  5.074937e+08  3.008761e+08  1.781351e+08
3  1.188494e+07  7.926714e+08  9.485948e+08
4  6.071372e+08  3.236949e+08  4.464244e+08
5  1.744240e+08  4.062852e+08  4.456160e+08
6  7.622656e+07  9.790510e+08  7.587101e+08
7  8.762620e+08  1.298574e+08  4.487193e+08
8  6.262644e+08  4.648143e+08  5.947500e+08
9  5.951188e+08  9.744804e+08  8.572475e+08

In [14]: pd.set_option('float_format', '{:f}'.format)

In [15]: df
Out[15]: 
                 a                b                c
0 440554429.333866 142530512.999182 638719977.824965
1 879250168.522411 713590875.479215  46526045.819487
2 507493741.709532 300876106.387427 178135140.583541
3  11884941.851962 792671390.499431 948594814.816647
4 607137206.305609 323694879.619369 446424361.522071
5 174424035.448168 406285189.907148 445616045.754137
6  76226556.685384 979050957.963583 758710090.127867
7 876261954.607558 129857447.076183 448719292.453509
8 626264394.999419 464814260.796770 594750038.747595
9 595118819.308896 974480400.272515 857247528.610996

In [16]: df.describe()
Out[16]: 
                     a                b                c
count        10.000000        10.000000        10.000000
mean  479461624.877280 522785202.100082 536344333.626082
std   306428177.277935 320806568.078629 284507176.411675
min    11884941.851962 129857447.076183  46526045.819487
25%   240956633.919592 306580799.695412 445818124.696121
50%   551306280.509214 435549725.351959 521734665.600552
75%   621482597.825966 772901261.744377 728712562.052142
max   879250168.522411 979050957.963583 948594814.816647

End of edit

In [7]: df
Out[7]: 
              a             b             c
0  4.405544e+08  1.425305e+08  6.387200e+08
1  8.792502e+08  7.135909e+08  4.652605e+07
2  5.074937e+08  3.008761e+08  1.781351e+08
3  1.188494e+07  7.926714e+08  9.485948e+08
4  6.071372e+08  3.236949e+08  4.464244e+08
5  1.744240e+08  4.062852e+08  4.456160e+08
6  7.622656e+07  9.790510e+08  7.587101e+08
7  8.762620e+08  1.298574e+08  4.487193e+08
8  6.262644e+08  4.648143e+08  5.947500e+08
9  5.951188e+08  9.744804e+08  8.572475e+08

In [8]: df.describe()
Out[8]: 
                  a             b             c
count  1.000000e+01  1.000000e+01  1.000000e+01
mean   4.794616e+08  5.227852e+08  5.363443e+08
std    3.064282e+08  3.208066e+08  2.845072e+08
min    1.188494e+07  1.298574e+08  4.652605e+07
25%    2.409566e+08  3.065808e+08  4.458181e+08
50%    5.513063e+08  4.355497e+08  5.217347e+08
75%    6.214826e+08  7.729013e+08  7.287126e+08
max    8.792502e+08  9.790510e+08  9.485948e+08

You need to fiddle with the pandas.options.display.float_format attribute. Note, in my code I've used import pandas as pd. A quick fix is something like:

In [29]: pd.options.display.float_format = "{:.2f}".format

In [10]: df
Out[10]: 
             a            b            c
0 440554429.33 142530513.00 638719977.82
1 879250168.52 713590875.48  46526045.82
2 507493741.71 300876106.39 178135140.58
3  11884941.85 792671390.50 948594814.82
4 607137206.31 323694879.62 446424361.52
5 174424035.45 406285189.91 445616045.75
6  76226556.69 979050957.96 758710090.13
7 876261954.61 129857447.08 448719292.45
8 626264395.00 464814260.80 594750038.75
9 595118819.31 974480400.27 857247528.61

In [11]: df.describe()
Out[11]: 
                 a            b            c
count        10.00        10.00        10.00
mean  479461624.88 522785202.10 536344333.63
std   306428177.28 320806568.08 284507176.41
min    11884941.85 129857447.08  46526045.82
25%   240956633.92 306580799.70 445818124.70
50%   551306280.51 435549725.35 521734665.60
75%   621482597.83 772901261.74 728712562.05
max   879250168.52 979050957.96 948594814.82