如何将字典列表转换为数据帧?考虑到:
[{'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'}]
我想把上面的变成一个数据框架:
month points points_h1 time year
0 NaN 50 NaN 5:00 2010
1 february 25 NaN 6:00 NaN
2 january 90 NaN 9:00 NaN
3 june NaN 20 NaN NaN
注意:列的顺序不重要。
当前回答
我有下面的字典列表与datetime键和int值:
list = [{datetime.date(2022, 2, 10): 7}, {datetime.date(2022, 2, 11): 1}, {datetime.date(2022, 2, 11): 1}]
我有一个问题，将其转换为Dataframe与上面的方法，因为它创建了Dataframe与列的日期…
我的解决方案:
df = pd.DataFrame()
for i in list:
temp_df = pd.DataFrame.from_dict(i, orient='index')
df = df.append(temp_df)
其他回答
Pyhton3: 前面列出的大多数解决方案都有效。然而，在某些情况下，数据帧的row_number不需要，并且必须单独写入每一行(记录)。 下面的方法在这种情况下很有用。
import csv
my file= 'C:\Users\John\Desktop\export_dataframe.csv'
records_to_save = data2 #used as in the thread.
colnames = list[records_to_save[0].keys()]
# remember colnames is a list of all keys. All values are written corresponding
# to the keys and "None" is specified in case of missing value
with open(myfile, 'w', newline="",encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(colnames)
for d in records_to_save:
writer.writerow([d.get(r, "None") for r in colnames])
我发现的最简单的方法是这样的:
dict_count = len(dict_list)
df = pd.DataFrame(dict_list[0], index=[0])
for i in range(1,dict_count-1):
df = df.append(dict_list[i], ignore_index=True)
我有下面的字典列表与datetime键和int值:
list = [{datetime.date(2022, 2, 10): 7}, {datetime.date(2022, 2, 11): 1}, {datetime.date(2022, 2, 11): 1}]
我有一个问题，将其转换为Dataframe与上面的方法，因为它创建了Dataframe与列的日期…
我的解决方案:
df = pd.DataFrame()
for i in list:
temp_df = pd.DataFrame.from_dict(i, orient='index')
df = df.append(temp_df)
你也可以使用pd.DataFrame.from_dict(d) as:
In [8]: d = [{'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'}]
In [12]: pd.DataFrame.from_dict(d)
Out[12]:
month points points_h1 time year
0 NaN 50.0 NaN 5:00 2010.0
1 february 25.0 NaN 6:00 NaN
2 january 90.0 NaN 9:00 NaN
3 june NaN 20.0 NaN NaN
在pandas 16.2中，我必须执行pd. datafframe .from_records(d)才能使其工作。
