根据时间戳记间隔创建csv文件的数据帧 [英] Create a dataframe of csv files based on timestamp intervals

问题描述

我相信我的问题确实很简单，并且必须有一种非常简单的方法来解决此问题，但是由于我对Python相当陌生，尤其是熊猫，所以我无法自己解决它.

I believe that my problem is really straightforward and there must be a really easy way to solve this issue, however as I am quite new with Python, specially pandas, I could not sort it out by my own.

我有数百个具有以下格式的csv文件: text_2014-02-22_13-00-00

I have hundreds of csv files that are on the following format: text_2014-02-22_13-00-00

因此格式为 str_YY-MM-DD_HH-MI-SS .概括起来，每个文件代表一个小时的间隔.

So the format is str_YY-MM-DD_HH-MI-SS. And to sum up, every file represents a interval of one hour.

我想根据该间隔从我将用Start_Time和End_Time设置的间隔创建一个数据帧.因此，例如，如果我将Start_Time设置为2014-02-22 21:40:00并将End_Time设置为2014-02-22 22:55:00(我使用的时间格式只是为了说明该示例)，那么我将获得一个数据帧，该数据帧包含上述间隔之间的数据，该间隔来自两个不同的文件.

I want to create a dataframe based on the interval that I will set with Start_Time and End_Time, from that interval. So, if for example, I set Start_Time as 2014-02-22 21:40:00 and End_Time as 2014-02-22 22:55:00 (The time-format that I am using is just to illustrate the example), then I will get a dataframe which comprehends the data in between the aforementioned interval , which comes from two different files.

所以，我认为这个问题可能分为两个部分:

So, I believe that this problem might be divided into two parts:

1-从文件名中仅读取日期

1 - Read just the date out of the file name

2-根据我设置的时间间隔创建一个数据框.

2 - Create a dataframe based on the time interval that I set.

希望我能做到简洁明了.非常感谢您在此方面的帮助！也欢迎提出查询建议

Hope that I managed to be succinct and precise. I would really appreciate your help on this one! Suggestions of what to look up for are also welcome

推荐答案

解决方案有几个不同的部分.

The solution has a few different parts.

1. 创建文件夹的路径
2. 手动创建3个csv文件
3. 将csv文件保存到列表
4. 编写自定义函数以将文件名解析为日期时间对象
5. 将它们组合在一起，循环浏览文件夹中的csv文件

import os
import pandas as pd
import datetime

# step 1: create the path to folder
path_cwd = os.getcwd()

# step 2: manually 3 sample CSV files
df_1 = pd.DataFrame({'Length': [10, 5, 6],
                     'Width': [5, 2, 3],
                     'Weight': [100, 120, 110]
                    }).to_csv('text_2014-02-22_13-00-00.csv', index=False)
df_2 = pd.DataFrame({'Length': [11, 7, 8],
                     'Width': [4, 1, 2],
                     'Weight': [101, 111, 131]
                    }).to_csv('text_2014-02-22_14-00-00.csv', index=False)
df_3 = pd.DataFrame({'Length': [15, 9, 7],
                     'Width': [1, 4, 2],
                     'Weight': [200, 151, 132]
                    }).to_csv('text_2014-02-22_15-00-00.csv', index=False)

# step 3: save the contents of the folder to a list
list_csv = os.listdir(path_cwd)
list_csv = [x for x in list_csv if '.csv' in x]

print('here are the 3 CSV files in the folder: ')
print(list_csv)

# step 4: extract the datetime from filenames
def get_datetime_filename(str_filename):
    '''
    Function to grab the datetime from the filename.

    Example: 'text_2014-02-22_13-00-00.csv'
    '''
    # split the filename by the underscore
    list_split_file = str_filename.split('_')

    # the 2nd part is the date
    str_date = list_split_file[1]

    # the 3rd part is the time, remove the '.csv'
    str_time = list_split_file[2]
    str_time = str_time.split('.')[0]

    # combine the 2nd and 3rd parts
    str_datetime = str(str_date + ' ' + str_time)

    # convert the string to a datetime object
    # https://chrisalbon.com/python/basics/strings_to_datetime/
    # https://stackoverflow.com/questions/10663720/converting-a-time-string-to-seconds-in-python
    dt_datetime = datetime.datetime.strptime(str_datetime, '%Y-%m-%d %H-%M-%S')

    return dt_datetime

# Step 5: bring it all together

# create empty dataframe
df_master = pd.DataFrame()

# loop through each csv files 
for each_csv in list_csv:

    # full path to csv file
    temp_path_csv = os.path.join(path_cwd, each_csv)

    # temporary dataframe
    df_temp = pd.read_csv(temp_path_csv)

    # add a column with the datetime from filename
    df_temp['datetime_source'] = get_datetime_filename(each_csv)

    # concatenate dataframes
    df_master = pd.concat([df_master, df_temp])

# reset the dataframe index
df_master = df_master.reset_index(drop=True)

# examine the master dataframe
print(df_master.shape)
# print(df_master.head(10))
df_master.head(10)

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