Creating a new column based on if-elif-else condition

问题

I have a DataFrame df:

I want to create a new column based on the following criteria:

if row A == B: 0

if rowA > B: 1

if row A < B: -1

so given the above table, it should be:

    A    B    C
a   2    2    0
b   3    1    1
c   1    3   -1

For typical if else cases I do np.where(df.A > df.B, 1, -1), does pandas provide a special syntax for solving my problem with one step (without the necessity of creating 3 new columns and then combining the result)?

回答1:

To formalize some of the approaches laid out above:

Create a function that operates on the rows of your dataframe like so:

def f(row):
    if row['A'] == row['B']:
        val = 0
    elif row['A'] > row['B']:
        val = 1
    else:
        val = -1
    return val

Then apply it to your dataframe passing in the axis=1 option:

In [1]: df['C'] = df.apply(f, axis=1)

In [2]: df
Out[2]:
   A  B  C
a  2  2  0
b  3  1  1
c  1  3 -1

Of course, this is not vectorized so performance may not be as good when scaled to a large number of records. Still, I think it is much more readable. Especially coming from a SAS background.

回答2:

df.loc[df['A'] == df['B'], 'C'] = 0
df.loc[df['A'] > df['B'], 'C'] = 1
df.loc[df['A'] < df['B'], 'C'] = -1

Easy to solve using indexing. The first line of code reads like so, if column A is equal to column B then create and set column C equal to 0. Note I didn't try to run this so the code may need to be modified to the following, df.loc[(df['A'] == df['B']), 'C'] = 0, which uses parentheses around the condition.

回答3:

For this particular relationship, you could use np.sign:

>>> df["C"] = np.sign(df.A - df.B)
>>> df
   A  B  C
a  2  2  0
b  3  1  1
c  1  3 -1

回答4:

Lets say above one is your original dataframe and you want to add a new column 'old'

If age greater than 50 then we consider as older=yes otherwise False

step 1: Get the indexes of rows whose age greater than 50

row_indexes=df[df['age']>=50].index

step 2: Using .loc we can assign a new value to column

df.loc[row_indexes,'elderly']="yes"

same for age below less than 50

row_indexes=df[df['age']<50].index

df[row_indexes,'elderly']="no"

来源：https://stackoverflow.com/questions/21702342/creating-a-new-column-based-on-if-elif-else-condition

标签

python

pandas

conditional