You can use .replace(), as follows:

df = df.replace({np.inf: df[np.isfinite(df)].max().max(), 
                -np.inf: df[np.isfinite(df)].min().min()})

Here, df[np.isfinite(df)].max().max() and df[np.isfinite(df)].min().min() are the respective finite maximum and minimum of the dataframe. We replace np.inf and -np.inf with them respectively.

Demo

Data Input

df = pd.DataFrame({'Col1': [np.inf, -2000.0, 345.0], 'Col2': [1234.0, -np.inf, 890.0]})


     Col1    Col2
0     inf  1234.0
1 -2000.0    -inf
2   345.0   890.0

Output:

print(df)

     Col1    Col2
0  1234.0  1234.0
1 -2000.0 -2000.0
2   345.0   890.0

Edit

If you want to replace with min max of the particular column instead of the min max over the global dataframe, you can use nested dict in .replace(), as follows:

col_min_max = {np.inf: df[np.isfinite(df)].max(),    # column-wise max
              -np.inf: df[np.isfinite(df)].min()}    # column-wise min

df = df.replace({col: col_min_max for col in df.columns})

Demo

Data Input

df = pd.DataFrame({'Col1': [np.inf, -2000.0, 345.0], 'Col2': [1234.0, -np.inf, 890.0]})


     Col1    Col2
0     inf  1234.0
1 -2000.0    -inf
2   345.0   890.0

Output:

print(df)

     Col1    Col2
0   345.0  1234.0
1 -2000.0   890.0
2   345.0   890.0

inf and -inf are replaced by the respective max, min of the column accordingly.

Filter out inf values first and then get max of Series:

m = df.loc[df['Crime_Rate'] != np.inf, 'Crime_Rate'].max()
df['Crime_Rate'].replace(np.inf,m,inplace=True)

Another solution:

mask = df['Crime_Rate'] != np.inf
df.loc[~mask, 'Crime_Rate'] = df.loc[mask, 'Crime_Rate'].max()

print (df)
  City  Crime_Rate
0    A        10.0
1    B        20.0
2    C        20.0
3    D        15.0

One way is to first convert infs to NaNs with np.isinf masking and then NaNs to max values of rows with np.nanmax:

array[np.isinf(array)] = np.nan
array[np.isnan(array)] = np.nanmax(array, axis=1)

to get

>>> array

array([[ 1.,  2.,  3.,  5.,  5.],
       [10.,  9.,  8.,  7.,  6.],
       [ 4., 10.,  2.,  6.,  6.]])

You almost there:

df['Col_A'].replace([-np.inf],min(df['Col_A'].replace(-np.inf, 
np.nan)),inplace=True)