在数据帧中，计算条件在一列中发生的次数？

Question

背景

我有五年的NO2测量数据，在csv文件中，每个位置和年份一个文件。我已将所有文件以相同格式加载到数据帧中：

Date    Hour    Location    NO2_Level
0   01/01/2016  00  Street  18
1   01/01/2016  01  Street  39
2   01/01/2016  02  Street  129
3   01/01/2016  03  Street  76
4   01/01/2016  04  Street  40

目标

对于每个数据帧计数，NO2\u级别大于150的次数，并输出该值。你知道吗

所以我写了一个循环，从正确的目录创建所有的数据帧，并适当地清理它们。你知道吗

问题

无论我尝试过什么，我知道检查的结果都是不正确的，例如： -给定年份中每个位置的计数值相同（可能但不太可能） -在我知道计数应该有任何正数的一年里，每个位置都返回0

我尝试过的

我已经尝试了很多方法来获得每个数据帧的这个值，比如让列成为一个系列：

NO2_Level = pd.Series(df['NO2_Level'])
count = (NO2_Level > 150).sum()'''

使用pd计数（）：

count = df[df['NO2_Level'] >= 150].count()

这两种方法最接近我想要输出的内容

测试示例

data = {'Date': ['01/01/2016','01/02/2016',' 01/03/2016', '01/04/2016', '01/05/2016'], 'Hour': ['00', '01', '02', '03', '04'], 'Location':  ['Street','Street','Street','Street','Street',], 'NO2_Level': [18, 39, 129, 76, 40]}
df = pd.DataFrame(data=d)
NO2_Level = pd.Series(df['NO2_Level'])
count = (NO2_Level > 150).sum()
count

预期产出

因此，我试图让它为格式为Location，year，count（of condition）的每个数据帧输出一行：

Kirkstall Road,2013,47
Haslewood Close,2013,97
...
Jack Lane Hunslet,2015,158

所以上面的例子会产生

Street, 2016, 1

实际值 每年每个地点都会产生相同的结果，在某些年份（2014年），当检查时，计数似乎根本不起作用，应该有：

Kirkstall Road,2013,47
Haslewood Close,2013,47
Tilbury Terrace,2013,47
Corn Exchange,2013,47
Temple Newsam,2014,0
Queen Street Morley,2014,0
Corn Exchange,2014,0
Tilbury Terrace,2014,0
Haslewood Close,2015,43
Tilbury Terrace,2015,43
Corn Exchange,2015,43
Jack Lane Hunslet,2015,43
Norman Rows,2015,43

Answer 1

下面是一个随机生成的样本的解决方案：

def random_dates(start, end, n):
    start_u = start.value // 10 ** 9
    end_u = end.value // 10 ** 9
    return pd.to_datetime(np.random.randint(start_u, end_u, n), unit='s')

location = ['street', 'avenue', 'road', 'town', 'campaign']

df = pd.DataFrame({'Date' : random_dates(pd.to_datetime('2015-01-01'), pd.to_datetime('2018-12-31'), 20),
                   'Location' : np.random.choice(location, 20),
                   'NOE_level' : np.random.randint(low=130, high= 200, size=20)})

---

#Keep only year for Date
df['Date'] = df['Date'].dt.strftime("%Y")

print(df)

df = df.groupby(['Location', 'Date'])['NOE_level'].apply(lambda x: (x>150).sum()).reset_index(name='count')
print(df)

生成的df示例：

        Date  Location  NOE_level
0       2018      town        191
1       2017  campaign        187
2       2017      town        137
3       2016    avenue        148
4       2017  campaign        195
5       2018      town        181
6       2018      road        187
7       2018      town        184
8       2016      town        155
9       2016    street        183
10      2018      road        136
11      2017      road        171
12      2018    street        165
13      2015    avenue        193
14      2016  campaign        170
15      2016    street        132
16      2016  campaign        165
17      2015      road        161
18      2018      road        161
19      2015      road        140 

输出：

    Location       Date  count
0     avenue       2015      1
1     avenue       2016      0
2   campaign       2016      2
3   campaign       2017      2
4       road       2015      1
5       road       2017      1
6       road       2018      2
7     street       2016      1
8     street       2018      1
9       town       2016      1
10      town       2017      0
11      town       2018      3

Answer 2

希望这有帮助。你知道吗

import pandas as pd

ddict = {
    'Date':['2016-01-01','2016-01-01','2016-01-01','2016-01-01','2016-01-01','2016-01-02',],
    'Hour':['00','01','02','03','04','02'],
    'Location':['Street','Street','Street','Street','Street','Street',],
    'N02_Level':[19,39,129,76,40, 151],
}

df = pd.DataFrame(ddict)

# Convert dates to datetime
df['Date'] = pd.to_datetime(df['Date'])

# Make a Year column
df['Year'] = df['Date'].apply(lambda x: x.strftime('%Y'))

# Group by lcoation and year, count by M02_Level > 150
df1 = df[df['N02_Level'] > 150].groupby(['Location','Year']).size().reset_index(name='Count')

# Interate the results
for i in range(len(df1)):
    loc = df1['Location'][i]
    yr = df1['Year'][i]
    cnt = df1['Count'][i]
    print(f'{loc},{yr},{cnt}')


### To not use f-strings
for i in range(len(df1)):
    print('{loc},{yr},{cnt}'.format(loc=df1['Location'][i], yr=df1['Year'][i], cnt=df1['Count'][i]))

样本数据：

        Date Hour Location  N02_Level
0 2016-01-01   00   Street         19
1 2016-01-01   01   Street         39
2 2016-01-01   02   Street        129
3 2016-01-01   03   Street         76
4 2016-01-01   04   Street         40
5 2016-01-02   02   Street        151

输出：

Street,2016,1