Python 如何从 Pandas 中的两列形成元组列
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How to form tuple column from two columns in Pandas
提问by elksie5000
I've got a Pandas DataFrame and I want to combine the 'lat' and 'long' columns to form a tuple.
我有一个 Pandas DataFrame，我想将 'lat' 和 'long' 列组合起来形成一个元组。
<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)
The code I tried to use was:
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)
However, this returned the following error:
但是，这返回了以下错误：
---------------------------------------------------------------------------
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
How can I solve this problem?
我怎么解决这个问题？
采纳答案by Dale Jung
Get comfortable with zip. It comes in handy when dealing with column data.
适应zip. 它在处理列数据时派上用场。
df['new_col'] = list(zip(df.lat, df.long))
It's less complicated and faster than using applyor map. Something like np.dstackis twice as fast as zip, but wouldn't give you tuples.
它比使用applyor更简单、更快捷map。like 的np.dstack速度是 的两倍zip，但不会给你元组。
回答by Wouter Overmeire
In [10]: df
Out[10]:
A B lat long
0 1.428987 0.614405 0.484370 -0.628298
1 -0.485747 0.275096 0.497116 1.047605
2 0.822527 0.340689 2.120676 -2.436831
3 0.384719 -0.042070 1.426703 -0.634355
4 -0.937442 2.520756 -1.662615 -1.377490
5 -0.154816 0.617671 -0.090484 -0.191906
6 -0.705177 -1.086138 -0.629708 1.332853
7 0.637496 -0.643773 -0.492668 -0.777344
8 1.109497 -0.610165 0.260325 2.533383
9 -1.224584 0.117668 1.304369 -0.152561
In [11]: df['lat_long'] = df[['lat', 'long']].apply(tuple, axis=1)
In [12]: df
Out[12]:
A B lat long lat_long
0 1.428987 0.614405 0.484370 -0.628298 (0.484370195967, -0.6282975278)
1 -0.485747 0.275096 0.497116 1.047605 (0.497115615839, 1.04760475074)
2 0.822527 0.340689 2.120676 -2.436831 (2.12067574274, -2.43683074367)
3 0.384719 -0.042070 1.426703 -0.634355 (1.42670326172, -0.63435462504)
4 -0.937442 2.520756 -1.662615 -1.377490 (-1.66261469102, -1.37749004179)
5 -0.154816 0.617671 -0.090484 -0.191906 (-0.0904840623396, -0.191905582481)
6 -0.705177 -1.086138 -0.629708 1.332853 (-0.629707821728, 1.33285348929)
7 0.637496 -0.643773 -0.492668 -0.777344 (-0.492667604075, -0.777344111021)
8 1.109497 -0.610165 0.260325 2.533383 (0.26032456699, 2.5333825651)
9 -1.224584 0.117668 1.304369 -0.152561 (1.30436900612, -0.152560909725)
回答by Ted Petrou
Pandas has the itertuplesmethod to do exactly this:
Pandas 有itertuples办法做到这一点：
list(df[['lat', 'long']].itertuples(index=False, name=None))
回答by user3820991
I'd like to add df.values.tolist(). (as long as you don't mind to get a column of lists rather than tuples)
我想补充df.values.tolist()。（只要你不介意得到一列列表而不是元组）
import pandas as pd
import numpy as np
size = int(1e+07)
df = pd.DataFrame({'a': np.random.rand(size), 'b': np.random.rand(size)})
%timeit df.values.tolist()
1.47 s ± 38.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit list(zip(df.a,df.b))
1.92 s ± 131 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
