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[英]How do I find the value in one dataframe column in the row with the maximum value of another column?
[英]How do I find the maximum value in an array within a dataframe column?
我有一個如下所示的數據幀(df):
a b
loc.1 [1, 2, 3, 4, 7, 5, 6]
loc.2 [3, 4, 3, 7, 7, 8, 6]
loc.3 [1, 4, 3, 1, 7, 8, 6]
...
我想在列b中找到數組的最大值,並將其附加到原始數據幀。 我的想法是這樣的:
for line in df:
split = map(float,b.split(','))
count_max = max(split)
print count
理想的輸出應該是:
a b max_val
loc.1 [1, 2, 3, 4, 7, 5, 6] 7
loc.2 [3, 4, 3, 7, 7, 8, 6] 8
loc.3 [1, 4, 3, 1, 7, 8, 6] 8
...
但這不起作用,因為我不能使用b.split,因為它沒有定義...
如果使用沒有NaN
的列表最好是在列表理解或map
使用max
:
a['max'] = [max(x) for x in a['b']]
a['max'] = list(map(max, a['b']))
純熊貓解決方案:
a['max'] = pd.DataFrame(a['b'].values.tolist()).max(axis=1)
樣品 :
array = {'loc.1': np.array([ 1,2,3,4,7,5,6]),
'loc.2': np.array([ 3,4,3,7,7,8,6]),
'loc.3': np.array([ 1,4,3,1,7,8,6])}
L = [(k, v) for k, v in array.items()]
a = pd.DataFrame(L, columns=['a','b']).set_index('a')
a['max'] = [max(x) for x in a['b']]
print (a)
b max
a
loc.1 [1, 2, 3, 4, 7, 5, 6] 7
loc.2 [3, 4, 3, 7, 7, 8, 6] 8
loc.3 [1, 4, 3, 1, 7, 8, 6] 8
編輯:
您還可以獲得list comprehension
max
:
L = [(k, v, max(v)) for k, v in array.items()]
a = pd.DataFrame(L, columns=['a','b', 'max']).set_index('a')
print (a)
b max
a
loc.1 [1, 2, 3, 4, 7, 5, 6] 7
loc.2 [3, 4, 3, 7, 7, 8, 6] 8
loc.3 [1, 4, 3, 1, 7, 8, 6] 8
您可以使用numpy
數組進行矢量化計算:
df = pd.DataFrame({'a': ['loc.1', 'loc.2', 'loc.3'],
'b': [[1, 2, 3, 4, 7, 5, 6],
[3, 4, 3, 7, 7, 8, 6],
[1, 4, 3, 1, 7, 8, 6]]})
df['maxval'] = np.array(df['b'].values.tolist()).max(axis=1)
print(df)
# a b maxval
# 0 loc.1 [1, 2, 3, 4, 7, 5, 6] 7
# 1 loc.2 [3, 4, 3, 7, 7, 8, 6] 8
# 2 loc.3 [1, 4, 3, 1, 7, 8, 6] 8
嘗試這個:
df["max_val"] = df["b"].apply(lambda x:max(x))
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