Suppose I have the following array in python:
[
{'id': [1,2,3]},
{'name': [4,3,2]},
{'age': [9,0,1]},
]
How would I load this into a pandas dataframe? Usually I do pd.DataFrame from a dict, but it's important for me to maintain the column order.
The final data should look like this:
id name age
1 4 9
2 3 0
3 2 1
You can construct a single dictionary and then feed to pd.DataFrame. To guarantee column ordering is preserved, use collections.OrderedDict:
from collections import OrderedDict
L = [{'id': [1,2,3]},
{'name': [4,3,2]},
{'age': [9,0,1]}]
df = pd.DataFrame(OrderedDict([(k, v) for d in L for k, v in d.items()]))
print(df)
id name age
0 1 4 9
1 2 3 0
2 3 2 1
With Python 3.7+ dictionaries are insertion ordered, so you can use a regular dict:
df = pd.DataFrame({k: v for d in L for k, v in d.items()})
Or merge the list of dictionaries (source) and convert the result to a dataframe:
merged_data = {}
[merged_data.update(d) for d in original_data]
# or, b/c it's more pythonic:
# list(map(lambda x: merged_data.update(x), original_data))
df = pd.DataFrame.from_dict(merged_data)
df = df[['id', 'name', 'age']]
print(df)
# id name age
# 0 1 4 9
# 1 2 3 0
# 2 3 2 1
For me it's more clear and readable.
A little hacky, but does
pd.concat([pd.DataFrame(d_) for d_ in d], axis=1)
work?
(assuming
d = your_list
)
axis=0 is the default, and will concatenate dataframes vertically (ie if you have two dataframes of three columns and three rows, axis=0 will make one with three columns and six rows. axis=1 is the opposite, concatenates them horizontally, so you would have 6 columns and 3 rows. Since in this case you are creating 3 new dataframes of 3 rows and 1 column, to get the resulting 3x3 df you need to use axis=1. (more infor here: pandas.pydata.org/pandas-docs/version/0.23.4/generated/…)