[英]Combining multiple data-frame columns
I am trying to combine 2 data frames columns into 1 but when I try to do it based on specific size the second data-frame column doesn't copy correctly. 我正在尝试将2个数据帧列合并为1,但是当我尝试根据特定大小进行操作时,第二个数据帧列无法正确复制。
I have tried the code below as pasted below. 我已经尝试了下面粘贴下面的代码。
import pandas as pd
def readDataFile():
fileName = "year.csv"
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
dfY = pd.read_csv(fileName, parse_dates=['date'], date_parser=dateparse)
fileName = "month.csv"
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
dfM = pd.read_csv(fileName, parse_dates=['date'], date_parser=dateparse)
newDF = pd.DataFrame()
newDF['date_y'] = dfY['date']
newDF['year_y_n'] = dfY['Y_N']
newDF['date_m'] = dfM['date'][len(dfM) - len(dfY):len(dfM)]
newDF['year_y_n'] = dfM['Y_N'][len(dfM) - len(dfY):len(dfM)]
print newDF
readDataFile()
File: month.csv 档案:month.csv
date,Y_N
2018-03-14 04:00:00,N
2018-04-03 04:00:00,N
2018-05-31 04:00:00,Y
2018-06-14 04:00:00,N
2018-07-30 04:00:00,N
2018-08-31 04:00:00,Y
2018-09-28 04:00:00,N
2018-10-10 04:00:00,N
2018-11-07 04:00:00,Y
2018-12-31 04:00:00,N
2019-01-31 04:00:00,N
2019-02-05 04:00:00,Y
2019-03-29 04:00:00,N
2019-04-30 04:00:00,Y
2019-05-03 04:00:00,N
2019-06-03 04:00:00,Y
File: year.csv 文件:year.csv
date,Y_N
2014-05-23 04:00:00,Y
2015-12-21 04:00:00,N
2016-05-03 04:00:00,Y
2017-12-20 04:00:00,N
2018-06-14 04:00:00,N
2019-06-25 04:00:00,N
These are the CURRENT results: 这些是当前结果:
date_y year_y_n date_m month_y_n
0 2014-05-23 04:00:00 Y NaT NaN
1 2015-12-21 04:00:00 N NaT NaN
2 2016-05-03 04:00:00 Y NaT NaN
3 2017-12-20 04:00:00 N NaT NaN
4 2018-06-14 04:00:00 N NaT NaN
5 2019-06-25 04:00:00 N NaT NaN
Expected results are: 预期结果是:
date_y year_y_n date_m month_y_n
2014-05-23 04:00:00 Y 2019-01-31 04:00:00 N
2015-12-21 04:00:00 N 2019-02-05 04:00:00 Y
2016-05-03 04:00:00 Y 2019-03-29 04:00:00 N
2017-12-20 04:00:00 N 2019-04-30 04:00:00 Y
2018-06-14 04:00:00 N 2019-05-03 04:00:00 N
2019-06-25 04:00:00 N 2019-06-03 04:00:00 Y
Let's say you have an arbitrary number of dataframes dfA
, dfB
, dfC
, etc. You want to merge them, but they're different sizes. 假设您有任意数量的数据帧
dfA
, dfB
, dfC
等。您想合并它们,但是它们的大小不同。 The most basic approach is to concatenate them: 最基本的方法是将它们串联起来:
df = pd.concat([dfA, dfB, dfC], axis=1)
But if the dataframes are different sizes, there will be missing rows. 但是,如果数据帧的大小不同,则会缺少行。 If you don't care which rows are preserved, you can just drop rows with missing values:
如果您不在乎保留哪些行,则可以删除缺少值的行:
df.dropna()
But if you specifically want to use the last N rows of each dataframe, where N is the length of the smallest dataframe, you need to do a little more work. 但是,如果您特别想使用每个数据帧的最后N行,其中N是最小数据帧的长度,则需要做更多的工作。 But I'll wait and see if that's what you want.
但是,我将等待,看看这是否是您想要的。
Old Answer: 旧答案:
Merging can be much simpler than this. 合并可能比这简单得多。 Use
pd.merge
: 使用
pd.merge
:
pd.merge(dfY, dfM[-len(dfY):].reset_index(),
suffixes=['_y', '_m'], left_index=True, right_index=True)
dfM[-len(dfY):]
gets the last N rows of dfM
, where N is the length of dfY
. dfM[-len(dfY):]
得到的最后N行dfM
,其中N是长度dfY
。 .reset_index()
makes the index of the subset of dfM
start at 0, so it can correctly align with dfY
. .reset_index()
使.reset_index()
的子集的dfM
从0开始,因此它可以与dfY
正确对齐。 suffixes=['_y', '_m']
keeps the column names different. suffixes=['_y', '_m']
使列名保持不同。 You can rename these if you like. The problem was related to the index. 问题与索引有关。 If you run the code below:
如果您运行下面的代码:
newDF = pd.DataFrame()
newDF['date_y'] = dfY['date']
print(newDF)
You will get the output: 您将获得输出:
date_y
0 2014-05-23 04:00:00
1 2015-12-21 04:00:00
2 2016-05-03 04:00:00
3 2017-12-20 04:00:00
4 2018-06-14 04:00:00
5 2019-06-25 04:00:00
The index is starting from 0 索引从0开始
And running this: 并运行此:
newDF = pd.DataFrame()
newDF['date_m'] = dfM['date'][len(dfM) - len(dfY):len(dfM)]
print(newDF)
You will get the output: 您将获得输出:
date_m
10 2019-01-31 04:00:00
11 2019-02-05 04:00:00
12 2019-03-29 04:00:00
13 2019-04-30 04:00:00
14 2019-05-03 04:00:00
15 2019-06-03 04:00:00
Here, the index is starting from 10 在这里,索引从10开始
So, you need to reset the index of the columns 'date' and 'Y_N' of dfM dataframe,like this: 因此,您需要重置dfM数据帧的“日期”和“ Y_N”列的索引,如下所示:
def readDataFile():
fileName = "year.csv"
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
dfY = pd.read_csv(fileName, parse_dates=['date'], date_parser=dateparse)
fileName = "month.csv"
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
dfM = pd.read_csv(fileName, parse_dates=['date'], date_parser=dateparse)
newDF = pd.DataFrame()
newDF['date_y'] = dfY['date']
newDF['year_y_n'] = dfY['Y_N']
# Changes made on this line.
newDF['date_m'] = dfM['date'][len(dfM) - len(dfY):len(dfM)].reset_index(drop=True)
newDF['month_y_n'] = dfM['Y_N'][len(dfM) - len(dfY):len(dfM)].reset_index(drop=True)
print(newDF)
readDataFile()
Output: 输出:
date_y year_y_n date_m month_y_n
0 2014-05-23 04:00:00 Y 2019-01-31 04:00:00 N
1 2015-12-21 04:00:00 N 2019-02-05 04:00:00 Y
2 2016-05-03 04:00:00 Y 2019-03-29 04:00:00 N
3 2017-12-20 04:00:00 N 2019-04-30 04:00:00 Y
4 2018-06-14 04:00:00 N 2019-05-03 04:00:00 N
5 2019-06-25 04:00:00 N 2019-06-03 04:00:00 Y
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