Dictionary of lists to dataframe

your_dict = {
    'key1': [10, 100.1, 0.98, 1.2],
    'key2': [72.5],
    'key3': [1, 5.2, 71.2, 9, 10.11, 12.21, 65, 7]
}

pd.concat({k: pd.Series(v) for k, v in your_dict.items()})

key1  0     10.00
      1    100.10
      2      0.98
      3      1.20
key2  0     72.50
key3  0      1.00
      1      5.20
      2     71.20
      3      9.00
      4     10.11
      5     12.21
      6     65.00
      7      7.00
dtype: float64

Or with axis=1

your_dict = {
    'key1': [10, 100.1, 0.98, 1.2],
    'key2': [72.5],
    'key3': [1, 5.2, 71.2, 9, 10.11, 12.21, 65, 7]
}

pd.concat({k: pd.Series(v) for k, v in your_dict.items()}, axis=1)

     key1  key2   key3
0   10.00  72.5   1.00
1  100.10   NaN   5.20
2    0.98   NaN  71.20
3    1.20   NaN   9.00
4     NaN   NaN  10.11
5     NaN   NaN  12.21
6     NaN   NaN  65.00
7     NaN   NaN   7.00

I suggest you just create a dict of Series, since your keys do not have the same number of values:

{ key: pd.Series(val) for key, val in x.items() }

You can then do Pandas operations on each column individually.

Once you have that, if you really want a DataFrame, you can:

pd.DataFrame({ key: pd.Series(val) for key, val in x.items() })

     key1  key2   key3
0   10.00  72.5   1.00
1  100.10   NaN   5.20
2    0.98   NaN  71.20
3    1.20   NaN   9.00
4     NaN   NaN  10.11
5     NaN   NaN  12.21
6     NaN   NaN  65.00
7     NaN   NaN   7.00

d={
    'key1': [10, 100.1, 0.98, 1.2],
    'key2': [72.5],
    'key3': [1, 5.2, 71.2, 9, 10.11, 12.21, 65, 7]
}

df=pd.DataFrame.from_dict(d,orient='index').transpose()

Then df is

    key3    key2    key1
0   1.00    72.5    10.00
1   5.20    NaN     100.10
2   71.20   NaN     0.98
3   9.00    NaN     1.20
4   10.11   NaN     NaN

Note that numpy has some built in functions that can do calculations ignoring NaN values, which may be relevant here. For example, if you want to find the mean of 'key1' column, you can do it as follows:

import numpy as np
np.nanmean(df[['key1']])
28.07

Other useful functions include numpy.nanstd, numpy.nanvar, numpy.nanmedian, numpy.nansum.

EDIT: Note that the functions from your basic functions link can also handle nan values. However, their estimators may be different from those of numpy. For example, they calculate the unbiased estimator of sample variance, while the numpy version calculates the "usual" estimator of sample variance.