How to define conditional for pandas column of several last row in data frame?

Question

Supposed I have a data frame with these rows as the last 8 row.

time                a       b       b           d           e           f 
2018-03-04 10:00:00 86.0    194.0   1.084830    1.088466    196.000000  84.333333
2018-03-04 10:30:00 37.0    59.0    1.082257    1.091397    203.000000  87.833333
2018-03-04 11:00:00 65.0    117.0   1.068825    1.091043    220.166667  96.666667
2018-03-04 11:30:00 10.0    9.0     1.070807    1.087203    183.666667  82.333333
2018-03-04 12:00:00 94.0    157.0   1.083382    1.077549    112.833333  61.666667
2018-03-04 12:30:00 66.0    68.0    1.075636    1.077623    100.666667  59.666667
2018-03-04 13:00:00 224.0   607.0   1.152262    1.088861    169.500000  82.666667
2018-03-04 13:30:00 112.0   279.0   1.119430    1.095057    206.166667  95.166667

How do I create a new column "g" using this condition on Pandas: If the row is the last row, value is 100%, If the row is the 2nd last row, value is 95%.. until it reach 70%, else it will be 0?

What do you base your percentage on ? Is it 100% if column f ? — vlemaistre
– vlemaistre, Commented Jul 2, 2019 at 15:54
@vlemaistre 100% will refer to the last row in the data frame, 95% to the 2nd last row, etc. and not based on other value in other column. — npm
– npm, Commented Jul 2, 2019 at 15:56

Quang Hoang · Accepted Answer · 2019-07-02 16:06:47Z

5

IIUC, g is not already in df.columns, so we can do:

vals = np.arange(0.7,1,0.05)
df['g'] = 0
df.iloc[-len(vals):, -1] = vals

answered Jul 2, 2019 at 16:06

Quang Hoang

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1 Comment

npm Over a year ago

Sorry to get back but can you explain to me what does: data.iloc[-len(vals):, -1] = vals mean?

samhanndean · Accepted Answer · 2019-07-02 16:32:11Z

Looking at the answer above, I thought of not posting this, but anyways -

Assuming you created a dataframe -

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy','Jason', 'Molly', 'Tina', 'Jake', 'Amy','Jason', 'Molly', 'Tina', 'Jake', 'Amy','Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
        'age': [42, 52, 36, 24, 73, 42, 52, 36, 24, 73, 42, 52, 36, 24, 73, 42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 4, 24, 31, 2, 3],
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])
df

It gives out a dataframe -

Now create a new list that can be added as the new column in our dataframe -

indexList = df.index.tolist()

listToBeInsertedInNewColumn = []

newElement = 100
i = len(indexList)-1
listToBeInsertedInNewColumn.append(str(newElement)+"%")
while i >= 1:
    newElement -= 5
    if newElement >= 70:
        listToBeInsertedInNewColumn.append(str(newElement)+"%")
    else:
        listToBeInsertedInNewColumn.append("0%")
    i -= 1

listToBeInsertedInNewColumn.reverse()

And then finally add this to the dataframe -

df['g'] = list(listToBeInsertedInNewColumn)

This will also give you what you asked for in the question -

It's not as clean as the original answer, but still, an answer.

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