python - for if 循环根据条件进行分类

Question

我是 python 的新手并且曾经使用 R。对于这个问题，我会使用 as.factor 并根据数字进行分类。

早些时候，我曾尝试使用 replace 和 .loc 函数，以便根据条件在新列中提供新的类别值，但它只会在我想做的事情上运行失败。

最终我创建了以下非常简单的函数:

g['Category'] = ""

for i in g['NumFloorsGroup']:
    if i == '0-9' or i == '10-19':
        g['Category'] = 'LowFl'
    elif i == '50~':
        g['Category'] = 'HighFl'
    else:
        g['Category'] = 'NormalFl'

但是当我运行该函数时，它只返回“LowFl”并且不更正其他部分。我觉得我错过了什么。

数据信息如下:

<class 'pandas.core.frame.DataFrame'>
Int64Index: 596 entries, 128 to 595
Data columns (total 4 columns):
YearBuilt         596 non-null int64
NumFloorsGroup    596 non-null category
Count             596 non-null int64
Category          596 non-null object
dtypes: category(1), int64(2), object(1)

任何评论都会有帮助!

bins = [0, 10, 20, 30, 40, 50, np.inf]
labels = ['0-9', '10-19', '20-29', '30-39', '40-49', '50~']
copy = original_data.copy()
copy['NumFloorsGroup'] = pd.cut(copy['NumFloors'], bins=bins, labels=labels, include_lowest=True)

g = (copy.groupby(['YearBuilt', 'NumFloorsGroup'])['YearBuilt']
        .count()
        .reset_index(name="Count")
                 .sort_values(by='Count', ascending=False))

以及只返回LowFl的部分

g['Category'] = ""

for i in g['NumFloorsGroup']:
    if i == '0-9' or i == '10-19':
        g['Category'] = 'LowFl'
    elif i == '50~':
        g['Category'] = 'HighFl'
    else:
        g['Category'] = 'NormalFl'

这会将所有类别返回为 LowFl

    YearBuilt   NumFloorsGroup  Count   Category
128 1920    0-9 90956   LowFl
171 1930    0-9 76659   LowFl
144 1925    0-9 70387   LowFl
237 1950    0-9 47237   LowFl
91  1910    0-9 46384   LowFl

Answer 1

我建议更改 cut使用新的 bins 和新的标签来运行，因为最好的办法是避免 pandas 中的循环，因为如果存在一些矢量化函数，速度会很慢:

df = pd.DataFrame({'Floors':[0,1,10,19,20,25,40, 70]})

bins = [0, 10, 20, 30, 40, 50, np.inf]
labels = ['0-9', '10-19', '20-29', '30-39', '40-49', '50~']

df['NumFloorsGroup'] = pd.cut(df['Floors'], 
                              bins=bins, 
                              labels=labels,
                              include_lowest=True)

df['Category'] = pd.cut(df['Floors'], 
                        bins=[0, 19, 50, np.inf], 
                        labels=['LowFl','NormalFl','HighFl'],
                        include_lowest=True)

print (df)
   Floors NumFloorsGroup  Category
0       0            0-9     LowFl
1       1            0-9     LowFl
2      10            0-9     LowFl
3      19          10-19     LowFl
4      20          10-19  NormalFl
5      25          20-29  NormalFl
6      40          30-39  NormalFl
7      70            50~    HighFl

或者使用map与字典一起 fillna用 NormalFl 替换不在字典中的值 (NaNs):

d = { "0-9": 'LowFl',  "10-19": 'LowFl',"50+": 'HighFl'}
df['Category']  = df['NumFloorsGroup'].map(d).fillna('NormalFl')