Pandas - Cleaning Data

"Wrong data" does not have to be "empty cells" or "wrong format", it can  
just be wrong, like if someone registered "199" instead of "1.99".
Sometimes you can spot wrong data by looking at the data set, because you have an expectation of what 
it should be.
If you take a look at our data set, you can see that in row 7, the duration is 450, but for all the other rows the duration is between 30 and 60.
It doesn't have to be wrong, but taking in consideration that this is the data set of someone's workout 
sessions,
we conclude with the fact that this person did not work out in 450 minutes.

      Duration          Date  Pulse  Maxpulse  Calories
  0         60  '2020/12/01'    110       130     409.1
  1         60  '2020/12/02'    117       145     479.0
  2         60  '2020/12/03'    103       135     340.0
  3         45  '2020/12/04'    109       175     282.4
  4         45  '2020/12/05'    117       148     406.0
  5         60  '2020/12/06'    102       127     300.0
  6         60  '2020/12/07'    110       136     374.0
  7        450  '2020/12/08'    104       134     253.3
  8         30  '2020/12/09'    109       133     195.1
  9         60  '2020/12/10'     98       124     269.0
  10        60  '2020/12/11'    103       147     329.3
  11        60  '2020/12/12'    100       120     250.7
  12        60  '2020/12/12'    100       120     250.7
  13        60  '2020/12/13'    106       128     345.3
  14        60  '2020/12/14'    104       132     379.3
  15        60  '2020/12/15'     98       123     275.0
  16        60  '2020/12/16'     98       120     215.2
  17        60  '2020/12/17'    100       120     300.0
  18        45  '2020/12/18'     90       112       NaN
  19        60  '2020/12/19'    103       123     323.0
  20        45  '2020/12/20'     97       125     243.0
  21        60  '2020/12/21'    108       131     364.2
  22        45           NaN    100       119     282.0
  23        60  '2020/12/23'    130       101     300.0
  24        45  '2020/12/24'    105       132     246.0
  25        60  '2020/12/25'    102       126     334.5
  26        60      20201226    100       120     250.0
  27        60  '2020/12/27'     92       118     241.0
  28        60  '2020/12/28'    103       132       NaN
  29        60  '2020/12/29'    100       132     280.0
  30        60  '2020/12/30'    102       129     380.3
  31        60  '2020/12/31'     92       115     243.0


How can we fix wrong values, like the one for "Duration" in row 7?
One way to fix wrong values is to replace them with something else.
In our example, it is most likely a typo, and the value should be "45" instead of "450", and 
we 
could just insert "45" in row 7:
Set "Duration" = 45 in row 7:
For small data sets you might be able to replace the wrong data one by one, 
but not for big data sets.
To replace wrong data for larger data sets you can create some rules, e.g. 
set some boundaries for legal values, and replace any values that are outside of the 
boundaries.
Loop through all values in the "Duration" column.
If the value is higher than 120, set it to 120:
Another way of handling wrong data is to remove the rows that contains wrong data.
This way you do not have to find out what to replace them with, and there is 
a good chance you do not need them to do your analyses.
Delete rows where "Duration" is higher than 120:
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