import pandas as pd
lod = [{'points': 50, 'time': '5:00', 'year': 2010},
{'points': 25, 'time': '6:00', 'month': "february"},
{'points':90, 'time': '9:00', 'month': 'january'},
{'points_h1':20, 'month': 'june'}]
df = pd.DataFrame(lod)
print(df.describe(include=['float', 'object']))
'''
points time year month points_h1
count 3.000000 3 1.0 3 1.0
unique NaN 3 NaN 3 NaN
top NaN 9:00 NaN january NaN
freq NaN 1 NaN 1 NaN
mean 55.000000 NaN 2010.0 NaN 20.0
std 32.787193 NaN NaN NaN NaN
min 25.000000 NaN 2010.0 NaN 20.0
25% 37.500000 NaN 2010.0 NaN 20.0
50% 50.000000 NaN 2010.0 NaN 20.0
75% 70.000000 NaN 2010.0 NaN 20.0
max 90.000000 NaN 2010.0 NaN 20.0
'''
from collections import Counter
d=[{'key1': 'valueA', 'key2': 'valueB'}, {'key1': 'valueC', 'key3': 'valueD'}, {'key1': 'valueC'}]
keys=[]
for i in d:
for j in i.keys():
keys.append(j)
Counter(keys)
keys = dict()
# d is your dictionary
for i in d:
for k, v in i.items():
if k in keys:
keys[k] += 1
else:
keys[k] = 1
max = 0
max_key = ''
for k, v in keys.items():
if v > max:
max = v
max_key = k
print(max, max_key)
您可以使用
pandas
将其转换为parse the dict,然后再转换为describe it to get stats但是请注意,这不适用于嵌套字典!您需要使用
pip
安装pandas
这是你的答案吗
输出
使用以下命令:
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