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[英]How to convert a python list to pandas Dataframe in python
[英]How to convert a Python list to into pandas DataFrame :
我有下面的列表,我已经简化了:
my_list = ['select', 'fruit1', 'fruit2, 'fruit3', 'from', 'basket1',
'select', 'fruit4', 'from', 'basket2',
'select', 'fruit5', 'fruit6' 'from', 'basket3', ..... so on]
请注意我的列表如何包含“选择”和“来自”语句。
我想要实现的 output 是 DataFrame 或者说 Excel Z78E6221F6393D14CE5668:
Fruit number Basket number
fruit1 basket1
fruit2 basket1
fruit3 basket1
fruit4 basket2
fruit5 basket3
fruit6 basket3
. .
. .
. .
. .
有没有办法达到这个结果? 我已经尝试了很多东西,但它不会工作.. :(
类似下面的东西(使用一个简单的“状态机”)
import pandas as pd
lst = ['select', 'fruit1', 'fruit2', 'fruit3', 'from', 'basket1',
'select', 'fruit4', 'from', 'basket2',
'select', 'fruit5', 'fruit6', 'from', 'basket3']
data = []
fruits = []
state = 'select'
for word in lst:
if word == 'select':
state = 'select'
continue
if word == 'from':
state = 'basket'
continue
if state == 'select':
fruits.append(word)
if state == 'basket':
for f in fruits:
data.append({'fruit':f,'basket':word})
fruits = []
df = pd.DataFrame(data)
print(df)
output
fruit basket
0 fruit1 basket1
1 fruit2 basket1
2 fruit3 basket1
3 fruit4 basket2
4 fruit5 basket3
5 fruit6 basket3
有很多方法可以做到这一点。 这种方法获取所有“来自”的索引,并使用np.split
将 2 个空格向前拆分,以便每个新数组的开头都是一个“选择”。 最后一个是空的,所以我们将删除它。
然后你可以通过分割每个数组来构建一个字典,并从中制作一个 dataframe 。
import numpy as np
import pandas as pd
my_list = ['select', 'fruit1', 'fruit2', 'fruit3', 'from', 'basket1',
'select', 'fruit4', 'from', 'basket2',
'select', 'fruit5', 'fruit6', 'from', 'basket3']
f = [i+2 for i, x in enumerate(my_list) if x == "from"][:-1]
s = np.split(my_list,f)
df = pd.DataFrame([{'basket':q[-1],'fruits':q[1:-2]} for q in s])
df = df.explode('fruits')
Output
basket fruits
0 basket1 fruit1
0 basket1 fruit2
0 basket1 fruit3
1 basket2 fruit4
2 basket3 fruit5
2 basket3 fruit6
data = {'Select' : {'Fruit_Number':
['fruit1','fruit2','fruit3']},'From' : {'Basket_Number':
['basket1','basket2','basket3']}}
data2 = data['Select']
data3 = data['From']
df2 = pd.DataFrame.from_dict(data2)
df3 = pd.DataFrame.from_dict(data3)
l = [df2,df3]
df_all = pd.concat(l,axis=1)
Fruit_Number Basket_Number
0 fruit1 basket1
1 fruit2 basket2
2 fruit3 basket3
制作一个通用且可重复使用的split
function ,就像这个问题的答案中的那样。 然后更容易从每个拆分组中产生对。
def split(sequence, sep):
group = []
for item in sequence:
if item == sep:
yield group
group = []
else:
group.append(item)
yield group
def parse_select(tokens):
for group in split(tokens, "select"):
for item in group[:-2]:
yield item, group[-1]
import pandas as pd
print(pd.DataFrame(parse_select(my_list)))
或者:
def parse_select(tokens):
for group in split(tokens, "select"):
if group:
items, (basket,) = split(group, "from")
for item in items:
yield item, basket
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