使用Python合并excel表格的两列

2023-08-30 12:11| 来源: 网络整理| 查看: 265

I have a 20 x 4000 dataframe in python using pandas. 我在Python中使用熊猫有20 x 4000数据帧。 Two of these columns are named Year and quarter. 这些列中的两个分别命名为Year和Quarter。 I'd like to create a variable called period that makes Year = 2000 and quarter= q2 into 2000q2 我想创建一个称为period的变量，使Year = 2000 and Quarter = q2变成2000q2

Can anyone help with that? 有人可以帮忙吗？

#1楼

参考：https://stackoom.com/question/1JJ5t/在pandas-python中的数据框中合并两列文本

#2楼 dataframe["period"] = dataframe["Year"].map(str) + dataframe["quarter"] #3楼 df = pd.DataFrame({'Year': ['2014', '2015'], 'quarter': ['q1', 'q2']}) df['period'] = df[['Year', 'quarter']].apply(lambda x: ''.join(x), axis=1)

Yields this dataframe 产生此数据框

Year quarter period 0 2014 q1 2014q1 1 2015 q2 2015q2

This method generalizes to an arbitrary number of string columns by replacing df[['Year', 'quarter']] with any column slice of your dataframe, eg df.iloc[:,0:2].apply(lambda x: ''.join(x), axis=1) . 通过将df[['Year', 'quarter']]替换为数据帧的任何列切片，例如df.iloc[:,0:2].apply(lambda x: ''.join(x), axis=1) 。

You can check more information about apply() method here 您可以在此处查看有关apply（）方法的更多信息

#4楼

Although the @silvado answer is good if you change df.map(str) to df.astype(str) it will be faster: 尽管如果将df.map(str)更改为df.map(str) ， df.astype(str) silvado答案很好，但它会更快：

import pandas as pd df = pd.DataFrame({'Year': ['2014', '2015'], 'quarter': ['q1', 'q2']}) In [131]: %timeit df["Year"].map(str) 10000 loops, best of 3: 132 us per loop In [132]: %timeit df["Year"].astype(str) 10000 loops, best of 3: 82.2 us per loop #5楼

The method cat() of the .str accessor works really well for this: .str访问器的cat()方法对此非常有效：

>>> import pandas as pd >>> df = pd.DataFrame([["2014", "q1"], ... ["2015", "q3"]], ... columns=('Year', 'Quarter')) >>> print(df) Year Quarter 0 2014 q1 1 2015 q3 >>> df['Period'] = df.Year.str.cat(df.Quarter) >>> print(df) Year Quarter Period 0 2014 q1 2014q1 1 2015 q3 2015q3

cat() even allows you to add a separator so, for example, suppose you only have integers for year and period, you can do this: cat()甚至允许您添加一个分隔符，因此，例如，假设年份和期间只有整数，则可以执行以下操作：

>>> import pandas as pd >>> df = pd.DataFrame([[2014, 1], ... [2015, 3]], ... columns=('Year', 'Quarter')) >>> print(df) Year Quarter 0 2014 1 1 2015 3 >>> df['Period'] = df.Year.astype(str).str.cat(df.Quarter.astype(str), sep='q') >>> print(df) Year Quarter Period 0 2014 1 2014q1 1 2015 3 2015q3

Joining multiple columns is just a matter of passing either a list of series or a dataframe containing all but the first column as a parameter to str.cat() invoked on the first column (Series): 连接多列只是传递一系列列表或包含除第一列之外的所有列的数据str.cat()作为在第一列（系列）上调用的str.cat()的参数的问题：

>>> df = pd.DataFrame( ... [['USA', 'Nevada', 'Las Vegas'], ... ['Brazil', 'Pernambuco', 'Recife']], ... columns=['Country', 'State', 'City'], ... ) >>> df['AllTogether'] = df['Country'].str.cat(df[['State', 'City']], sep=' - ') >>> print(df) Country State City AllTogether 0 USA Nevada Las Vegas USA - Nevada - Las Vegas 1 Brazil Pernambuco Recife Brazil - Pernambuco - Recife

Do note that if your pandas dataframe/series has null values, you need to include the parameter na_rep to replace the NaN values with a string, otherwise the combined column will default to NaN. 请注意，如果您的pandas数据框/系列具有空值，则需要包括参数na_rep以用字符串替换NaN值，否则合并的列将默认为NaN。

#6楼

Use of a lamba function this time with string.format(). 这次通过string.format（）使用lamba函数。

import pandas as pd df = pd.DataFrame({'Year': ['2014', '2015'], 'Quarter': ['q1', 'q2']}) print df df['YearQuarter'] = df[['Year','Quarter']].apply(lambda x : '{}{}'.format(x[0],x[1]), axis=1) print df Quarter Year 0 q1 2014 1 q2 2015 Quarter Year YearQuarter 0 q1 2014 2014q1 1 q2 2015 2015q2

This allows you to work with non-strings and reformat values as needed. 这使您可以根据需要使用非字符串并重新格式化值。

import pandas as pd df = pd.DataFrame({'Year': ['2014', '2015'], 'Quarter': [1, 2]}) print df.dtypes print df df['YearQuarter'] = df[['Year','Quarter']].apply(lambda x : '{}q{}'.format(x[0],x[1]), axis=1) print df Quarter int64 Year object dtype: object Quarter Year 0 1 2014 1 2 2015 Quarter Year YearQuarter 0 1 2014 2014q1 1 2 2015 2015q2

来源于：https://blog.csdn.net/xfxf996/article/details/80930414。

注：来源于这篇博主，手残退出找不到这篇文章了。

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