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# Creating a new column based on other columns in Pandas DataFrame

schedule Aug 10, 2023
Last updated
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PythonPandas
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To create a new column based on other columns, either:

• use column-arithmetics for fastest performance.

• use NumPy's where(~) method for creating binary columns

• use the apply(~) method, which is the slowest but offers the most flexibility

• use the Series' replace(~) method for mapping new values from existing columns.

# Creating new columns using arithmetics

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4],"B":[5,6]}, index=["a","b"])
df
A B
a 3 5
b 4 6

The fastest and simplest way of creating a new column is to use simple column-arithmetics:

df["C"] = df["A"] + df["B"]
df
A B C
a 3 5 8
b 4 6 10

For slightly more complicated operations, use the DataFrame's native methods:

df["C"] = df.max(axis=1)
df
A B C
a 3 5 5
b 4 6 6

Note the following:

• we are populating the new column C with the maximum of each row (axis=1).

• the return type of df.max(axis=1) is Series.

# Creating binary column values

Consider the following Pandas DataFrame:

df = pd.DataFrame({'name':['Alex','Bob','Cathy'],'age':[20,30,40]})
name age
0 Alex 20
1 Bob 30
2 Cathy 40

To create a new column of binary values that are based on the age column, use NumPy's where(~) method:

df['status'] = np.where(df['age'] < 25, 'JUNIOR', 'SENIOR')
name age status
0 Alex 20 JUNIOR
1 Bob 30 SENIOR
2 Cathy 40 SENIOR

Here, the first argument of the where(~) method is a boolean mask. If the boolean value is True, then resulting value will be 'JUNIOR', otherwise the value will be 'SENIOR'.

# Creating column with multiple values

Once again, consider the following Pandas DataFrame:

df = pd.DataFrame({'name':['Alex','Bob','Cathy'],'age':[20,30,40]})
name age
0 Alex 20
1 Bob 30
2 Cathy 40

To create a new column with multiple values based on the age column, use the apply(~) function:

def my_func(row):
if row['age'] < 25:
val = 'JUNIOR'
elif row['age'] < 35:
val = 'MID-LEVEL'
else:
val = 'SENIOR'
return val

df['status'] = df.apply(my_func, axis=1)
name age status
0 Alex 20 JUNIOR
1 Bob 30 MID-LEVEL
2 Cathy 40 SENIOR

Here, the apply(~) function is iteratively called for each row, and takes in as argument a Series representing a row.

# Creating column via mapping

Consider the same Pandas DataFrame as before:

df = pd.DataFrame({'name':['Alex','Bob','Cathy'],'age':[20,30,40]})
name age
0 Alex 20
1 Bob 30
2 Cathy 40

To create a new column that is based on some mapping of an existing column:

mapping = {
'Alex': 'ALEX',
'Bob': 'BOB',
'Cathy': 'CATHY'
}
df['upper_name'] = df['name'].replace(mapping)
name age upper_name
0 Alex 20 ALEX
1 Bob 30 BOB
2 Cathy 40 CATHY

# Creating column using the assign method

Consider the following Pandas DataFrame:

df = pd.DataFrame({"A":[3,4],"B":[5,6]}, index=["a","b"])
df
A B
a 3 5
b 4 6

We could also use the DataFrame's assign(~) method, which takes in as argument a function with the DataFrame as the input and returns the new column values:

def foo(df):
if df["A"].sum() > df["B"].sum():
return [-1,-1]
else:
return [0,0]

df.assign(C=foo)
A B C
0 3 5 0
1 4 6 0

Note the following:

• if the sum of column A is larger than that of column B, then [-1,-1] will be used as the new column, otherwise [0,0] will be used.

• the keyword argument (C) became the new column label.

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