I've seen similar questions, but never one that gives a simple straightforward pythonic answer.
I'm simply trying to get the average for the "high" column in a csv file.
import csv
import numpy as np
with open('2010-Jan-June.csv', 'r', encoding='utf8', newline='') as f:
highs = []
for row in csv.DictReader(f, delimiter=','):
high = int(row['high'])
print(sum(highs)/len(highs))
My csv looks like this:
date,high,low,precip
1-Jan,43,41,0
2-Jan,50,25,0
3-Jan,51,25,0
4-Jan,44,25,0
5-Jan,36,21,0
6-Jan,39,20,0
7-Jan,47,21,0.04
8-Jan,30,14,0
9-Jan,30,12,0
Using Pandas:
import pandas as pd
avg = pd.read_csv(r'/path/to/2010-Jan-June.csv', usecols=['high'], squeeze=True).mean()
Note, this is totally possible using plain Python:
import csv
import statistics as stats
with open('2010-Jan-June.csv') as f:
avg = stats.mean(row['high'] for row in csv.DictReader(f, delimiter=','))
print(avg)
Since you imported numpy
you can use that - almost as easily as pandas
:
Reading from a paste copy of your sample:
In [36]: txt="""date,high,low,precip
...: 1-Jan,43,41,0
...: 2-Jan,50,25,0
...: 3-Jan,51,25,0
...: 4-Jan,44,25,0
...: 5-Jan,36,21,0
...: 6-Jan,39,20,0
...: 7-Jan,47,21,0.04
...: 8-Jan,30,14,0
...: 9-Jan,30,12,0"""
Python3 with numpy 1.14 likes to have the encoding
parameter:
In [38]: data = np.genfromtxt(txt.splitlines(),delimiter=',',dtype=None,names=True,
...: encoding=None)
In [39]: data
Out[39]:
array([('1-Jan', 43, 41, 0. ), ('2-Jan', 50, 25, 0. ),
('3-Jan', 51, 25, 0. ), ('4-Jan', 44, 25, 0. ),
('5-Jan', 36, 21, 0. ), ('6-Jan', 39, 20, 0. ),
('7-Jan', 47, 21, 0.04), ('8-Jan', 30, 14, 0. ),
('9-Jan', 30, 12, 0. )],
dtype=[('date', '<U5'), ('high', '<i8'), ('low', '<i8'), ('precip', '<f8')])
The result is a structured array, from which it is easy to pick the high
field:
In [40]: data['high']
Out[40]: array([43, 50, 51, 44, 36, 39, 47, 30, 30])
In [41]: data['high'].mean()
Out[41]: 41.111111111111114
Or in one line, loading just one column:
In [44]: np.genfromtxt(txt.splitlines(),delimiter=',',skip_header=1,usecols=[1]).mean()
Out[44]: 41.111111111111114
Here is my attempt at a pythonic answer using just csv library...
import csv
with open ('names.csv') as csvfile:
reader = csv.DictReader(csvfile)
print sum(float(d['high']) for d in reader) / (reader.line_num - 1)
Will have a divide by 0 if there are no lines in the file.
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.