pandas read csv with regex

Question

I have a folder trip_data contains many csv file with date, which looks like this:

trip_data/
├── df_trip_20140803_1.csv
├── df_trip_20140803_2.csv
├── df_trip_20140803_3.csv
├── df_trip_20140803_4.csv
├── df_trip_20140803_5.csv
├── df_trip_20140803_6.csv
├── df_trip_20140804_1.csv
├── df_trip_20140804_2.csv
├── df_trip_20140804_3.csv
├── df_trip_20140804_4.csv
├── df_trip_20140804_5.csv
├── df_trip_20140804_6.csv
├── df_trip_20140805_1.csv
├── df_trip_20140805_2.csv
├── df_trip_20140805_3.csv
├── df_trip_20140805_4.csv
├── df_trip_20140805_5.csv
├── df_trip_20140805_6.csv
├── df_trip_20140806_1.csv
├── df_trip_20140806_2.csv
├── df_trip_20140806_3.csv
├── df_trip_20140806_4.csv

Now I want to load all these file separately by date with python pandas, means 4 DataFrame df_traip_20140803, df_traip_20140804, df_traip_20140805, df_traip_20140806

My code looks like this:

days = [20140803,20140804,20140805,20140806]

for day in days:
    ## Locate to the path
    path ='./trip_data/df_trip_%d*.csv' % day
    df = pd.read_csv(path, header=None, nrows=10,
                        names=['ID','lat','lon','status','timestamp'])

Which could not get the correct result. How can I do this?

MaxU - stand with Ukraine · Accepted Answer · 2017-01-03 14:21:26Z

I would collect all those CSV into dictionary of DataFrames with the following structure:

df['20140803'] - DF containing concatenated data belonging to all df_trip_20140803_*.csv CSV files.

Solution:

import os
import re
import glob
import pandas as pd

fpattern = r'D:\temp\.data\41444939\df_trip_{}_{}.csv'
files = glob.glob(fpattern.format('*','*'))

dates = sorted(set([re.split(r'_(\d{8})_(\d+)\.(\w+)', f)[1] for f in files]))

dfs = {}
for d in dates:
    dfs[d] = pd.concat((pd.read_csv(f) for f in glob.glob(fpattern.format(d, '*'))), ignore_index=True)

Test:

In [95]: dfs.keys()
Out[95]: dict_keys(['20140804', '20140805', '20140803', '20140806'])

In [96]: dfs['20140803']
Out[96]:
    a  b  c
0   0  0  7
1   3  7  1
2   9  7  3
3   7  4  7
4   5  2  4
5   0  0  4
6   7  2  2
7   8  4  1
8   0  8  3
9   3  9  0
10  7  3  9
11  1  9  8
12  6  7  2
13  3  8  1
14  3  4  5
15  0  9  2
16  5  8  7
17  8  5  4
18  2  0  2
19  9  6  6
20  6  6  6
21  2  6  9
22  1  0  8
23  3  1  1
24  7  4  2
25  7  4  2
26  8  3  7
27  7  3  2
28  1  7  7
29  3  6  5

Setup:

fn = r'D:\temp\.data\41444939\a.txt'
base_dir = r'D:\temp\.data\41444939'
files = open(fn).read().splitlines()
for f in files:
    pd.DataFrame(np.random.randint(0, 10, (5, 3)), columns=list('abc')) \
      .to_csv(os.path.join(base_dir, f), index=False)

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