[英]Using simple code to get the average (in Python) of an entire column in a csv file
我見過類似的問題,但是從來沒有人給出過簡單直接的pythonic答案。
我只是想獲取csv文件中“高”列的平均值。
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))
我的csv看起來像這樣:
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
使用熊貓:
import pandas as pd
avg = pd.read_csv(r'/path/to/2010-Jan-June.csv', usecols=['high'], squeeze=True).mean()
請注意,使用純Python完全可以實現:
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)
由於您導入了numpy
您可以像使用pandas
一樣輕松地使用它:
從樣本的粘貼副本中讀取:
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"""
numpy 1.14的Python3喜歡使用encoding
參數:
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')])
結果是一個結構化的數組,從中可以輕松選擇high
場:
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
或者一行,只加載一列:
In [44]: np.genfromtxt(txt.splitlines(),delimiter=',',skip_header=1,usecols=[1]).mean()
Out[44]: 41.111111111111114
這是我嘗試使用csv庫的pythonic答案...
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)
如果文件中沒有行,則除以0。
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