Pandas: how to change all the values of a column?

Question

I have a data frame with a column called \"Date\" and want all the values from this column to have the same value (the year only). Example:

City         


        

Answer 1

You can do a column transformation by using apply

Define a clean function to remove the dollar and commas and convert your data to float.

def clean(x):
    x = x.replace("$", "").replace(",", "").replace(" ", "")
    return float(x)

Next, call it on your column like this.

data['Revenue'] = data['Revenue'].apply(clean)

Answer 2

Or if one want to use lambda function in the apply function:

data['Revenue']=data['Revenue'].apply(lambda x:float(x.replace("$","").replace(",", "").replace(" ", "")))

Answer 3

As @DSM points out, you can do this more directly using the vectorised string methods:

df['Date'].str[-4:].astype(int)

Or using extract (assuming there is only one set of digits of length 4 somewhere in each string):

df['Date'].str.extract('(?P<year>\d{4})').astype(int)

An alternative slightly more flexible way, might be to use apply (or equivalently map) to do this:

df['Date'] = df['Date'].apply(lambda x: int(str(x)[-4:]))
             #  converts the last 4 characters of the string to an integer

The lambda function, is taking the input from the Date and converting it to a year.
You could (and perhaps should) write this more verbosely as:

def convert_to_year(date_in_some_format):
    date_as_string = str(date_in_some_format)  # cast to string
    year_as_string = date_in_some_format[-4:] # last four characters
    return int(year_as_string)

df['Date'] = df['Date'].apply(convert_to_year)

Perhaps 'Year' is a better name for this column...