human
Can you provide a the Python program you requested given the following:
4. "Date first observed" is a pretty weird column, but it seems like it has a date hiding inside. Using a function with .apply, transform the string (e.g. "20140324") into a Python date. Make the 0's show up as NaN.
assistant
#Here is the Python program you requested.
df['Date First Observed'][30: 35]

# 33 is a date
# Did it for the whole dataframe instead of for each row
# def year(row):
#     return df['Date First Observed'][row][:4]
# def month(row):
#     return df['Date First Observed'][row][4:6]
# def day(row):
#     return df['Date First Observed'][row][-2:]
# def date(row):
#     return {
#         'year': year(row),
#         'month': month(row),
#         'day': day(row)
#         }

def year(row):
    return row[:4]
def month(row):
    return row[4:6]
def day(row):
    return row[-2:]
def date(row):
    if len(row) == 8:
        dictionary = {
            'year': year(row),
            'month': month(row),
            'day': day(row)
            }
        return '{year}-{month}-{day}'.format(**dictionary)
    else:
        return None

df['Date First Observed Formatted'] = df['Date First Observed'].apply(date)

df['Date First Observed Formatted'][30: 35]