DataFrame的apply()、applymap()、map()方法

       对DataFrame对象中的某些行或列，或者对DataFrame对象中的所有元素进行某种运算或操作，我们无需利用低效笨拙的循环，DataFrame给我们分别提供了相应的直接而简单的方法，apply()和applymap()。其中apply()方法是针对某些行或列进行操作的，而applymap()方法则是针对所有元素进行操作的。

 1 map()方法

The map method works on series, so in our case, we will use it to transform a column of our DataFrame, which remember is just a pandas Series. Suppose that we decide that the class names are a bit long for our taste and we would like to code them using our special threeletter coding system. We'll use the map method with a Python dictionary as the argument toaccomplish this. We'll pass in a replacement for each of the unique iris types:

df['class'] = df['class'].map({'Iris-setosa': 'SET', 'Iris-virginica':'VIR', 'Iris-versicolor': 'VER'})
df

2 Apply（）方法

The apply method allows us to work with both DataFrames and Series. We'll start with an example that would work equally well with map, then we'll move on to examples that would work only with apply.

Using the iris DataFrame, let's make a new column based on the petal width. We previously saw that the mean for the petal width was 1.3. Let's now create a new column in our DataFrame, wide petal, that contains binary values based on the value in the petal width column. If the petal width is equal to or wider than the median, we will code it with a 1, and if it is less than the median, we will code it 0. We'll do this using the apply method on the petal width column:

df['wide petal'] = df['petal width'].apply(lambda v: 1 if v >= 1.3 else 0)
df

df['petal area'] = df.apply(lambda r: r['petal length'] * r['petal width'],axis=1)
df

3 Applymap（）方法

We've looked at manipulating columns and explained how to work with rows, but suppose that you'd like to perform a function across all data cells in your DataFrame; this is where applymap is the right tool. Let's take a look at an example:

df.applymap(lambda v: np.log(v) if isinstance(v, float) else v)

4 Groupby方法

df.groupby('class').mean()

df.groupby('petalwidth')['class'].unique().to_frame()

df.groupby('petalwidth')['class'].unique().to_frame()

df.groupby('petal width')['class'].unique().to_frame()

df.groupby('class').describe()

df.groupby('class')['petal width'].agg({'delta': lambda x: x.max() - x.min(), 'max': np.max, 'min': np.min})

   

简单来说，apply()方法 可以作用于DataFrame 还有Series， 作用于一行或者一列时，我们不妨可以采用，因为可以通过设置axis=0/1 来把握，demo如下：

applymap() 作用于每一个元素

map可以作用于Series每一个元素的

总的来说，map()、aply()、applymap()方法是一种对series、dataframe极其方便的应用与映射函数。

最后，非常重要的一点，这些映射函数，里面都是可以放入自定义函数的。

tips.head()

Out[34]:

	total_bill	tip	smoker	day	time	size	tip_pct
0	16.99	1.01	No	Sun	Dinner	2	0.059447
1	10.34	1.66	No	Sun	Dinner	3	0.160542
2	21.01	3.50	No	Sun	Dinner	3	0.166587
3	23.68	3.31	No	Sun	Dinner	2	0.139780
4	24.59	3.61	No	Sun	Dinner	4	0.146808

def top(df,n=5,column='tip_pct'):
    return df.sort_values(by=column)[-n:]

tips.groupby('smoker').apply(top)

Out[38]:

		total_bill	tip	smoker	day	time	size	tip_pct
smoker
No	88	24.71	5.85	No	Thur	Lunch	2	0.236746
	185	20.69	5.00	No	Sun	Dinner	5	0.241663
	51	10.29	2.60	No	Sun	Dinner	2	0.252672
	149	7.51	2.00	No	Thur	Lunch	2	0.266312
	232	11.61	3.39	No	Sat	Dinner	2	0.291990
Yes	109	14.31	4.00	Yes	Sat	Dinner	2	0.279525
	183	23.17	6.50	Yes	Sun	Dinner	4	0.280535
	67	3.07	1.00	Yes	Sat	Dinner	1	0.325733
	178	9.60	4.00	Yes	Sun	Dinner	2	0.416667
	172	7.25	5.15	Yes	Sun	Dinner	2	0.710345