如何从 Pandas 中的两列形成元组列 [英] How to form tuple column from two columns in Pandas

问题描述

我有一个 Pandas DataFrame，我想将 'lat' 和 'long' 列组合起来形成一个元组.

I've got a Pandas DataFrame and I want to combine the 'lat' and 'long' columns to form a tuple.

<class 'pandas.core.frame.DataFrame'>
Int64Index: 205482 entries, 0 to 209018
Data columns:
Month           205482  non-null values
Reported by     205482  non-null values
Falls within    205482  non-null values
Easting         205482  non-null values
Northing        205482  non-null values
Location        205482  non-null values
Crime type      205482  non-null values
long            205482  non-null values
lat             205482  non-null values
dtypes: float64(4), object(5)

我尝试使用的代码是:

def merge_two_cols(series): 
    return (series['lat'], series['long'])

sample['lat_long'] = sample.apply(merge_two_cols, axis=1)

然而，这返回了以下错误:

However, this returned the following error:

---------------------------------------------------------------------------
 AssertionError                            Traceback (most recent call last)
<ipython-input-261-e752e52a96e6> in <module>()
      2     return (series['lat'], series['long'])
      3 
----> 4 sample['lat_long'] = sample.apply(merge_two_cols, axis=1)
      5

...

AssertionError: Block shape incompatible with manager 

我该如何解决这个问题?

How can I solve this problem?

推荐答案

熟悉 zip.它在处理列数据时派上用场.

Get comfortable with zip. It comes in handy when dealing with column data.

df['new_col'] = list(zip(df.lat, df.long))

它比使用 apply 或 map 更简单、更快捷.np.dstack 之类的东西比 zip 快两倍，但不会给你元组.

It's less complicated and faster than using apply or map. Something like np.dstack is twice as fast as zip, but wouldn't give you tuples.

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