[英]Pandas - populate new column based on existing column values
I have the following dataframe df_shots
:我有以下 dataframe
df_shots
:
TableIndex MatchID GameWeek Player ... ShotPosition ShotSide Close Position
ShotsDetailID ...
6 5 46605 1 Roberto Firmino ... very close range N/A close very close rangeN/A
8 7 46605 1 Roberto Firmino ... the box the centre not close the boxthe centre
10 9 46605 1 Roberto Firmino ... the box the left not close the boxthe left
17 16 46605 1 Roberto Firmino ... the box the centre close the boxthe centre
447 446 46623 2 Roberto Firmino ... the box the centre close the boxthe centre
... ... ... ... ... ... ... ... ... ...
6656 6662 46870 27 Roberto Firmino ... very close range N/A close very close rangeN/A
6666 6672 46870 27 Roberto Firmino ... the box the right not close the boxthe right
6674 6680 46870 27 Roberto Firmino ... the box the centre not close the boxthe centre
6676 6682 46870 27 Roberto Firmino ... the box the left not close the boxthe left
6679 6685 46870 27 Roberto Firmino ... outside the box N/A not close outside the boxN/A
For the sake of clarity, all possible 'Position' values are:为了清楚起见,所有可能的“位置”值是:
positions = ['a difficult anglethe left',
'a difficult anglethe right',
'long rangeN/A',
'long rangethe centre',
'long rangethe left',
'long rangethe right',
'outside the boxN/A',
'penaltyN/A',
'the boxthe centre',
'the boxthe left',
'the boxthe right',
'the six yard boxthe left',
'the six yard boxthe right',
'very close rangeN/A']
Now I would to map the following x/y values to each 'Position' name, storing the value under a new 'Position XY' column:现在我将 map 为每个“位置”名称添加以下 x/y 值,并将该值存储在新的“位置 XY”列下:
the_boxthe_center = {'y':random.randrange(25,45), 'x':random.randrange(0,6)}
the_boxthe_left = {'y':random.randrange(41,54), 'x':random.randrange(0,16)}
the_boxthe_right = {'y':random.randrange(14,22), 'x':random.randrange(0,16)}
very_close_rangeNA = {'y':random.randrange(25,43), 'x':random.randrange(0,4)}
six_yard_boxthe_left = {'y':random.randrange(33,43), 'x':random.randrange(4,6)}
six_yard_boxthe_right = {'y':random.randrange(25,33), 'x':random.randrange(4,6)}
a_diffcult_anglethe_left = {'y':random.randrange(43,54), 'x':random.randrange(0,6)}
a_diffcult_anglethe_right = {'y':random.randrange(14,25), 'x':random.randrange(0,6)}
penaltyNA = {'y':random.randrange(36), 'x':random.randrange(8)}
outside_the_boxNA = {'y':random.randrange(14,54), 'x':random.randrange(16,28)}
long_rangeNA = {'y':random.randrange(0,68), 'x':random.randrange(40,52)}
long_rangethe_centre = {'y':random.randrange(0,68), 'x':random.randrange(28,40)}
long_rangethe_right = {'y':random.randrange(0,14), 'x':random.randrange(0,24)}
long_rangethe_left = {'y':random.randrange(54,68), 'x':random.randrange(0,24)}
I tried:我试过了:
if df_shots['Position']=='very close rangeN/A':
df_shots['Position X/Y']==very_close_rangeNA
...# and so on
But I get:但我得到:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
How do I do this?我该怎么做呢?
It's bad form to store so many related variables outside of a container, lets use a dictionary that we map to your dataframe.在容器外存储这么多相关变量是一种不好的形式,让我们使用字典,我们将 map 到您的 dataframe。
data_dict =
{'the boxthe centre': {'y':random.randrange(25,45)...}
df['Position'] = df['Position'].map(data_dict)
print(df['Position'])
6 {'y': 35, 'x': 2}
8 {'y': 32, 'x': 1}
10 {'y': 44, 'x': 11}
17 {'y': 32, 'x': 1}
447 {'y': 32, 'x': 1}
... NaN
6656 {'y': 35, 'x': 2}
6666 {'y': 15, 'x': 11}
6674 {'y': 32, 'x': 1}
6676 {'y': 44, 'x': 11}
6679 {'y': 37, 'x': 16}
Name: Position, dtype: object
Here is a bit of code that might do the trick you want.这里有一些代码可能会达到你想要的效果。
first create a list of all your "Position XY" like首先创建所有“位置 XY”的列表,例如
position_xy = [the_boxthe_center,the_boxthe_left,....,long_rangethe_left] #and so on...
and the correspondent positions
list (as you already have) then I propose you to do a dictionary so that every position does a correspondent position xy calculation和通讯员
positions
列表(正如你已经拥有的)然后我建议你做一个字典,以便每个 position 做一个通讯员 position xy 计算
dict_positionxy = dict(zip(position, position_xy))
then you create a new column in your dataframe, where you want to store the x,y values based on the position然后您在 dataframe 中创建一个新列,您要在其中存储基于 position 的 x、y 值
df_shots['Position X/Y'] = 0.
now you loop through all rows one by one现在你一一循环遍历所有行
for index, row in df_shots.iterrows():
for key, values in dict_positionxy.items():
if row['Position'] == key:
#row['Position X/Y'] = value
df_shots.at[index,’Position X/Y’]= value
print(df_shots)
This should do the trick:)这应该可以解决问题:)
Here's some sample code that accomplishes what you want.这是一些完成您想要的示例代码。 I created a basic mockup of df_shots, but this should run the same on your larger DataFrame.
我创建了一个基本的 df_shots 模型,但这应该在您较大的 DataFrame 上运行相同。 I've also stored some of those free variables in a
dict
to make filtering simpler.我还将其中一些自由变量存储在
dict
中,以使过滤更简单。
It should be noted, that because you pre-compute the random values of positions_xy
, all x/y values will be the same for each shot position.应该注意的是,因为您预先计算了
positions_xy
的随机值,所以每个镜头 position 的所有 x/y 值都是相同的。 This may or may not be what you intended.这可能是也可能不是您想要的。
import pandas as pd
import random
# Sample df_shots
df_shots = pd.DataFrame({'Position': ['the_boxthe_center', 'the_boxthe_left']})
# Store position/xy pairs in dict
positions_xy = {'the_boxthe_center': {'y': random.randrange(25, 45), 'x': random.randrange(0, 6)},
'the_boxthe_left': {'y': random.randrange(41, 54), 'x': random.randrange(0, 16)}}
# Create new column
df_shots['Position XY'] = ''
# Iterate over all position/xy pairs
for position, xy in positions_xy.items():
# Determine indices of all players that match
matches = df_shots['Position'] == position
matches_indices = matches[matches].index
# Update matching rows in df_shots with xy
for idx in matches_indices:
df_shots.at[idx, 'Position XY'] = xy
print(df_shots)
Outputs:输出:
Position Position XY
0 the_boxthe_center {'y': 36, 'x': 2}
1 the_boxthe_left {'y': 44, 'x': 0}
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