import numpy as np
np.round(p_table, decimals=2)

Try:

import pandas as pd
pd.set_option('display.precision', 2)

This causes it to use scientific (exponential) notation when appropriate, and keeps 2 decimal places. It makes the decision about whether to use scientific notation or not on a per-column basis, so if 1 value requires scientific notation, the whole column is displayed that way.

Examples:

0.0012             1.23e-03
0.0123             1.23e-02
100                1.00e+02
1234567890.123456  1.23e+09

You can set the display.precision option with e.g.:

pd.set_option('precision', 7)

when you are done, reset it back using

pd.reset_option('precision')

as documented in https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html

The round method only works as I think you want if the values in each column (i.e., in each pandas.Series) of the DataFrame already have more decimal points than the value you are passing to round.

For instance:

pd.Series([1.09185, 2.31476]).round(2)

returns:

0    1.09
1    2.31
dtype: float64

But if the Series has fewer decimal points than the number you are trying to round, you will not get the desired visual result. For instance:

pd.Series([1.6, 2.3]).round(2)

returns:

0    1.6
1    2.3
dtype: float64

This is mathematically correct, since the numbers in the second Series already have fewer decimal points than 2. But it is not what you visually expect.

If you only want to change the display of a Series or DataFrame inside a notebook, you should use pandas.set_option("display.precision", 2). This changes the visual representation of the Series or DataFrame, without changing the inner precision of the actual numbers.

If for some reason you need to save a Series or DataFrame with the numbers already with the desired decimal points, you can apply a function that converts the object to string type and formats the string:

pd.Series([1.6, 2.3]).apply(lambda x: f"{x:.2f}")

which returns a new Series of dtype object instead of float:

0    1.60
1    2.30
dtype: object

You can plug the display format into pandas' display options:

In [11]: df = pd.DataFrame(np.random.randn(2, 2))

In [12]: df
Out[12]:
          0         1
0  1.058814 -0.011675
1 -0.002627 -0.152505

In [13]: pd.options.display.float_format = '{:.2f}'.format

In [14]: df
Out[14]:
      0     1
0  1.06 -0.01
1 -0.00 -0.15

See more about python's string formatting here.

Note: the numbers themselves are unaffected (they haven't been rounded):

In [15]: df.iloc[0, 0]
Out[15]: 1.058814403984879

In [188]: df
Out[188]:
       a      b       c
0 1.0000 2.2460  2.0000
1 3.0000 4.4920  6.0000
2 5.0000 6.7380 10.0000

In [189]: pd.options.display.float_format = '{:,.2f}'.format

In [190]: df.apply(lambda x: x.astype(int) if np.allclose(x, x.astype(int)) else x)
Out[190]:
   a    b   c
0  1 2.25   2
1  3 4.49   6
2  5 6.74  10

UPDATE:

In [222]: df
Out[222]:
       0      1
0 3.0000 5.6000
1 1.2000 3.4560

In [223]: df.applymap(lambda x: str(int(x)) if abs(x - int(x)) < 1e-6 else str(round(x,2)))
Out[223]:
     0     1
0    3   5.6
1  1.2  3.46

NOTE: be aware that .applymap() method is pretty slow as it's doing map(func, series) for each series in the DataFrame