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

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

1. How do I print entire number in Python from describe() function?

converts the rest of the floats to strings using the `str.format` method to format the floats to 6 digits after the decimal point.

2. print entire number in Python from describe() function

converts the rest of the floats to strings using the `str.format` method to format the floats to 6 digits after the decimal point.

## Method 1

Suppose you have the following `DataFrame`:

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
```
```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```

## Method 2

```import numpy as np
import pandas as pd
np.random.seed(2016)
N = 4393476
df = pd.DataFrame(np.random.uniform(1e-4, 0.1, size=(N,3)), columns=list('ABC'))

desc = df.describe()
desc.loc['count'] = desc.loc['count'].astype(int).astype(str)
desc.iloc[1:] = desc.iloc[1:].applymap('{:.6f}'.format)
print(desc)
```

yields

```              A         B         C
count   4393476   4393476   4393476
mean   0.050039  0.050056  0.050057
std    0.028834  0.028836  0.028849
min    0.000100  0.000100  0.000100
25%    0.025076  0.025081  0.025065
50%    0.050047  0.050050  0.050037
75%    0.074987  0.075027  0.075055
max    0.100000  0.100000  0.100000
```

Under the hood, DataFrames are organized in columns. The values in a column can only have one data type (the column’s `dtype`). The DataFrame returned by `df.describe()` has columns of floating-point dtype:

```In [116]: df.describe().info()
<class 'pandas.core.frame.DataFrame'>
Index: 8 entries, count to max
Data columns (total 3 columns):
A    8 non-null float64
B    8 non-null float64
C    8 non-null float64
dtypes: float64(3)
memory usage: 256.0+ bytes
```

DataFrames do not allow you to treat one row as integers and the other rows as floats. However, if you change the contents of the DataFrame to strings, then you have full control over the way the values are displayed since all the values are just strings.

Thus, to create a DataFrame in the desired format, you could use

```desc.loc['count'] = desc.loc['count'].astype(int).astype(str)
```

to convert the `count` row to integers (by calling `astype(int)`), and then convert the integers to strings (by calling `astype(str)`). Then

```desc.iloc[1:] = desc.iloc[1:].applymap('{:.6f}'.format)
```

converts the rest of the floats to strings using the `str.format` method to format the floats to 6 digits after the decimal point.

Alternatively, you could use

```import numpy as np
import pandas as pd
np.random.seed(2016)
N = 4393476
df = pd.DataFrame(np.random.uniform(1e-4, 0.1, size=(N,3)), columns=list('ABC'))

desc = df.describe().T
desc['count'] = desc['count'].astype(int)
print(desc)
```

which yields

```     count      mean       std     min       25%       50%       75%  max
A  4393476  0.050039  0.028834  0.0001  0.025076  0.050047  0.074987  0.1
B  4393476  0.050056  0.028836  0.0001  0.025081  0.050050  0.075027  0.1
C  4393476  0.050057  0.028849  0.0001  0.025065  0.050037  0.075055  0.1
```

By transposing the `desc` DataFrame, the `count`s are now in their own column. So now the problem can be solved by converting that column’s dtype to `int`.

One advantage of doing it this way is that the values in `desc` remain numerical. So further calculations based on the numeric values can still be done.

I think this solution is preferrable, provided that the transposed format is acceptable.

## Summery

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.