You can use dt.strftime for formating datetimes and then custom format of floats:

df['time'] = df['time'].dt.strftime('%Y,%m,%d %H:%M:%S')

cols = ['price1','price2']
df[cols] = df[cols].applymap(lambda x: '{0:.4f}'.format(x))
print (df)
                  time  price1  price2
0  2018,02,01 00:00:00  1.4527  1.6548

Check out the top answer from

Pandas data precision

to see how to change the display precision if you want to see more decimal places. As has been said, the full precision is stored, just not displayed.

print(s) 
0       4.5678
1            5
2    7.987.998
Name: 0, dtype: object

print(type(s))
Out[152]: pandas.core.series.Series

Using str.extract + round:

r = s.str.extract('(\d+(?:\.\d+)?)', \
           expand=False).astype(float).round(2)
print(r)
0    4.57
1    5.00
2    7.99
Name: 0, dtype: float64

Unfortunately, the 5 cannot be an integer as your expected output describes, that would lead to mixed types and is generally discouraged.

For pandas >= 1.0:

<NA> type was introduced for 'Int64'. You can now do this:

df['your_column'].astype('Int64').astype('str')

And it will properly convert 1.0 to 1.

Alternative:

If you do not want to change the display options of all pandas, @maxymoo solution does, you can use apply:

df['your_column'].apply(lambda x: f'{x:.0f}')

Use:

• set_index for only numeric columns
• replace $ with one or more whitespaces \s+
• convert to floats by astype
• convert to custom format by applymap

df = (df.set_index('Names')
        .replace('\$\s+','', regex=True)
        .astype(float)
        .applymap('{:,.2f}'.format))
print (df)
         Cider  Juice Subtotal (Cider) Subtotal (Juice)   Total
Names                                                          
Richard  13.00   9.00            71.50            40.50  112.00
George    7.00  21.00            38.50            94.50  133.00
Paul      0.00  23.00             0.00           103.50  103.50
John     22.00   5.00           121.00            22.50  143.50
Total    42.00  58.00           231.00           261.00  492.00
Average  10.50  14.50            57.75            65.25  123.00

EDIT:

I try improve your solution:

people_ordered = input('How many people ordered? ') 

Data = []
# Create the 4x3 table from user input
for i in range(int(people_ordered)):
    names = input("Enter the name of Person #{}: ".format(i+1))  # type str

    cider_orderred = int(input("How many orders of cider did {} have? ".format(names)))  # type str -> int
    juice_orderred = int(input("How many orders of juice did {} have? ".format(names)))  # type str -> int

    #create in loop tuple and append to list Data
    Data.append((names, cider_orderred, juice_orderred))

#create DataFrame form list of tuples, create index by Names
df1 = pd.DataFrame(Data, columns=['Names','Cider','Juice']).set_index('Names')

#count all new columns, rows
df1['Subtotal(Cider)'] = df1['Cider'] * 5.5
df1['Subtotal(Juice)'] = df1['Juice'] * 4.5
df1['Total'] = df1['Subtotal(Cider)'] + df1['Subtotal(Juice)']
df1.loc['Total'] = df1.sum()
#remove row Total for correct mean
df1.loc['Average'] = df1.drop('Total').mean()

#get custom format of columns in list cols
cols = ['Subtotal(Cider)','Subtotal(Juice)','Total']
df1[cols] = df1[cols].applymap('$ {:,.2f}'.format)
#create column from index
df1 = df1.reset_index()

print(df1)
     Names  Cider  Juice Subtotal(Cider) Subtotal(Juice)     Total
0        r   13.0    9.0         $ 71.50         $ 40.50  $ 112.00
1        g    7.0   21.0         $ 38.50         $ 94.50  $ 133.00
2        p    0.0   23.0          $ 0.00        $ 103.50  $ 103.50
3        j   22.0    5.0        $ 121.00         $ 22.50  $ 143.50
4    Total   42.0   58.0        $ 231.00        $ 261.00  $ 492.00
5  Average   10.5   14.5         $ 57.75         $ 65.25  $ 123.00