在大熊猫数据框中，算出某一列中某条件发生的次数？

Question

Background

I have five years of NO2 measurement data, in csv files-one file for every location and year. I have loaded all the files into pandas dataframes in the same format:

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

Goal

For each dataframe count the number of times NO2_Level is greater than 150 and output this.

So I wrote a loop that's creates all the dataframes from the right directories and cleans them appropriately .

Problem

Whatever I've tried produces results I know on inspection are incorrect, eg : -the count value for every location on a given year is the same (possible but unlikely) -for a year when I know there should be any positive number for the count, every location returns 0

What I've tried

I have tried a lot of approaches to getting this value for each dataframe, such as making the column a series:

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

Using pd.count():

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

These two approaches have gotten closest to what I want to output

Example to test on

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

Expected Outputs

So from this I'm trying to get it to output a single line for each dataframe that was made in the format Location, year, count (of condition):

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

So the above example would produce

Street, 2016, 1

Actual Every year produces the same result for each location, for some years (2014) the count doesn't seem to work at all when on inspection there should be:

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

here is a solution with a sample generated (randomly):

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)

Example df generated:

        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 

output:

    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

Hopefully this helps.

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]))

Sample data:

        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

Output:

Street,2016,1