列和行操作Python Pandas

Question

这是我自己在Pandas中的第一个程序，我正在尝试按列和行执行一些csv操作。我有多个文件的过渡存储库。过渡存储库继续向其中添加文件。我正在尝试动态读取文件并执行一些操作，然后写入另一个文件夹中的主csv文件。

输入项

1. Folder_1: `Transition_Data`  


Test_1.csv, Test_2.csv

    Nos,Time,Count          Nos,Time,Count
    -------------------     ------------------
    2341,12:00:00,9865      1234,12:30:00,7865
    7352,12:00:00,8969      8435,12:30:00,7649

2. Folder2: Data_repository:Master_2.csv


    Nos,00:00:00
    ------------
    1234,1000
    8435,5243
    2341,563
    7352,345

3.Expected Output 

Nos,00:00:00,12:00:00,12:30:00
----------------------------------
1234,1000,0,6865
8435,5243,0,2406
2341,563,9302,0
7352,345,8624,0

阅读Nos从transition_data文件列，并检查其中Nos位于Master_2.csv该用创建新列Time作为新的头每次和减去col[2]的Transition_data文件用col[1]的Master_2.csv填充在新创建的列的新值，如果数据缺口填充0。我确实尝试了一些例子，但是我搞砸了。

程序更新如下所述，现在在通过文件读写路由逻辑时遇到问题

    import pandas as pd
    import os
    import numpy as np
    import glob

path_1 = '/Transition_Data/'
path_2 = 'Data_repository/Master_2.csv'

df_1 = pd.DataFrame(dict(Nos=Nos, Time=Time, Count=Count))

pivot = pd.pivot_table(path_1, '/.*CSV, index='Nos', columns='Time', values='Count')

df_master = pd.DataFrame('Master_2.csv', {'Nos':, '00:00:00':}).set_index('Nos')

result = df_master.join(pivot, how='inner')

result[result.columns[1:]] = result[result.columns[1:]].sub(result[result.columns[0]], axis=0)

result.fillna(0)

我尝试了上述程序，并收到以下错误

Traceback (most recent call last):
  File "read_test.py", line 19, in <module>
    df = pd.read_csv(filename, header='Count')
  File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 420, in parser_f
    return _read(filepath_or_buffer, kwds)
  File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 218, in _read
    parser = TextFileReader(filepath_or_buffer, **kwds)
  File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 502, in __init__
    self._make_engine(self.engine)
  File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 610, in _make_engine
    self._engine = CParserWrapper(self.f, **self.options)
  File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 972, in __init__
    self._reader = _parser.TextReader(src, **kwds)
  File "parser.pyx", line 476, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4538)
TypeError: an integer is required

Answer 1

我能想到的最简单的方法是将它们全部连接到一个 DataFrame 中，按时间对列进行排序，然后进行移位和减去以获得增量：

import pandas as pd
import os

path_1 = 'Transition_Data/'
path_2 = 'Data_repository/Master_2.csv'

# Read data, and combine "transition" data into 
# single joined data frame
master = pd.read_csv(path_2)
other_data = pd.concat([
        pd.read_csv(path_1 + f) for f in os.listdir(path_1)
    ])

# Index master data frame by Nos
master.set_index('Nos', inplace=True)

# Index transition data by Nos and Time
other_data.set_index(['Nos', 'Time'], inplace=True)

# Convert to series (to remove Count column heading)
# and unstack time to convert to columns
other_data = other_data['Count'].unstack('Time')

# Join the data sets on the Time axis
joined = pd.concat([master, other_data], axis=1)

# Sort the data sets by Time
joined = joined.sort_index(axis=1)

# Fill na values with data in previous period
joined = joined.fillna(method='pad',axis=1)

# Shift dataframe and subtract to get delta
delta = joined - joined.shift(axis=1).fillna(0)

print(delta)

这给出了您想要的输出：

      00:00:00  12:00:00  12:30:00
Nos                               
1234      1000         0      6865
2341       563      9302         0
7352       345      8624         0
8435      5243         0      2406