pandas adds a new column based on the existing column

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For a Dataframe in pandas, it is often necessary to create a new column based on one of the columns. For example, a common example: the level range needs to be determined based on the score. Let's take a look at how to achieve it.

def getlevel(score):
    if score < 60:
        return "bad"
    elif score < 80:
        return "mid"
    else:
        return "good"


def test():
    data = {'name': ['lili', 'lucy', 'tracy', 'tony', 'mike'],
            'score': [85, 61, 75, 49, 90]
            }
    df = pd.DataFrame(data=data)
    # 两种方式都可以
    # df['level'] = df.apply(lambda x: getlevel(x['score']), axis=1)
    df['level'] = df.apply(lambda x: getlevel(x.score), axis=1)

    print(df)

The result of running the above code

    name  score level
0   lili     85  good
1   lucy     61   mid
2  tracy     75   mid
3   tony     49   bad
4   mike     90  good

To achieve the above functions, the apply method in the dataframe is mainly used.
In the above code, a new column named level is added to the dataframe. The level is derived from the score column. If it is less than 60, it is bad, between 60-80 is mid, and above 80 is good.
Where axis=1 means that the rows of the original dataframe remain unchanged, and the dimensions of the columns have changed.