Question

我想使用Pandas和Python迭代我的.csv文件，并按季节对数据进行分组，计算一年中每个季节的平均值。目前季度剧本是Jan-Mar，Apr-Jun等。我希望季节与月份相关 - 11：'冬季'，12：'冬季'，1：'冬季'，2：'春天'，3：'春天'，4：'春天'，5：'夏天'，6：'夏天'，7：'夏天'，\ 8：'秋天'，9：'秋天'，10：'秋天'

我有以下数据：

Date,HAD
01/01/1951,1
02/01/1951,-0.13161201
03/01/1951,-0.271796132
04/01/1951,-0.258977158
05/01/1951,-0.198823057
06/01/1951,0.167794502
07/01/1951,0.046093808
08/01/1951,-0.122396694
09/01/1951,-0.121824587
10/01/1951,-0.013002463

到目前为止，这是我的代码：

# Iterate through a list of files in a folder looking for .csv files
for csvfilename in glob.glob("C:/Users/n-jones/testdir/output/*.csv"):

# Allocate a new file name for each file and create a new .csv file
    csvfilenameonly = "RBI-Seasons-Year" + path_leaf(csvfilename) 
    with open("C:/Users/n-jones/testdir/season/" + csvfilenameonly, "wb") as outfile:

        # Open the input csv file and allow the script to read it
        with open(csvfilename, "rb") as infile:

            # Create a pandas dataframe to summarise the data
            df = pd.read_csv(infile, parse_dates=[0], index_col=[0], dayfirst=True)

            mean = df.resample('Q-SEP', how='mean')

            # Output to new csv file
            mean.to_csv(outfile)

我希望这是有道理的。

提前谢谢！

Answer 1

看起来你只需要一个dict查找和一个groupby。下面的代码应该有效。

import pandas as pd
import os
import re

lookup = {
    11: 'Winter',
    12: 'Winter',
    1: 'Winter',
    2: 'Spring',
    3: 'Spring',
    4: 'Spring',
    5: 'Summer',
    6: 'Summer',
    7: 'Summer',
    8: 'Autumn',
    9: 'Autumn',
    10: 'Autumn'
}

os.chdir('C:/Users/n-jones/testdir/output/')

for fname in os.listdir('.'):
    if re.match(".*csv$", fname):
        data = pd.read_csv(fname, parse_dates=[0], dayfirst=True)
        data['Season'] = data['Date'].apply(lambda x: lookup[x.month])
        data['count'] = 1
        data = data.groupby(['Season'])['HAD', 'count'].sum()
        data['mean'] = data['HAD'] / data['count']
        data.to_csv('C:/Users/n-jones/testdir/season/' + fname)

使用python和pandas按季节分组数据

1 个答案: