You can use round:

df.lerate = df.lerate.round(2)

Example:

>>> df = pd.DataFrame(np.random.random([3, 3]), 
                      columns=['A', 'B', 'C'], index=['first', 'second', 'third'])
>>> df.A = df.A.round(2)
>>> df
           A         B         C
first   0.82  0.581855  0.548373
second  0.21  0.536690  0.986906
third   0.78  0.100343  0.576521

If you want to only modify the format of your values without doing any operation in pandas, you should just execute the following instruction:

pd.options.display.float_format = "{:,.2f}".format

This forces it not to use scientific notation (exponential notation) and always displays 2 places after the decimal point. It also adds commas.

You should be able to get more info here:

https://pandas.pydata.org/docs/user_guide/options.html#number-formatting

Examples:

0.0012             0.00
0.0123             0.01
1.2345             1.23
12.345             12.35
100                100.00
1234567890.123456  1,234,567,890.12
 

It seems you need DataFrame.round:

df = df.round(2)
print (df)
      NO  Topic A  Topic B  Topic C
0    0.0     1.00     1.00     1.00
1    1.0     0.55     0.64     0.55
2    2.0     0.57     0.74     0.68
3    3.0     0.85     0.86     0.85
4    4.0     0.20     0.20     0.20
5    5.0     0.85     0.84     0.85
6    6.0     0.45     0.53     0.45
7    7.0     0.62     0.66     0.70
8    8.0     0.57     0.50     0.57
9    9.0     0.85     0.90     0.88
10  10.0     0.95     0.97     0.96

Since 0.17.0 version you can do .round(n)

df.round(2)
      0     1     2     3
0  0.06  0.67  0.77  0.71
1  0.80  0.56  0.97  0.15
2  0.03  0.59  0.11  0.95
3  0.33  0.19  0.46  0.92

df
          0         1         2         3
0  0.057116  0.669422  0.767117  0.708115
1  0.796867  0.557761  0.965837  0.147157
2  0.029647  0.593893  0.114066  0.950810
3  0.325707  0.193619  0.457812  0.920403

Here is how you convert np.float64 columns to 2 decimal places

df_survey["some_column_with_too_many_decimal"] = df_survey["some_column_with_too_many_decimal"].apply(lambda x: int(x*100)/100)

Also to select only certain rows in that column if that is what you need, please use df.loc instead of iloc on every row, since the df might have too many rows.

df.loc[(df["column1"]>0), ["column2", "column3"]]

or

df.loc[(df["column1"]>0), "column2", "column3"]

The first argument to loc is a list of conditions to filter by, the second argument is the columns to select and then you can update them by using apply as show above.

If you want to use round, you can round off values then multiply by 100, convert to int and divide by 100 making it decimal with 2 places. The round function does not limit it to 2 decimal places because of the way the values are stored in the dataframe.