I have a dict
of lists
(which have variable lengths), I am looking forward to an efficient way of creating a Dataframe from it.
Assume I have minimum list length, so I can truncate size of bigger lists while creating Dataframe.
Here is my dummy code
data_dict = {'a': [1,2,3,4], 'b': [1,2,3], 'c': [2,45,67,93,82,92]}
min_length = 3
I can have a dictionary of 10k or 20k keys, so looking for an efficient way to create a DataFrame like bellow
>>> df
a b c
0 1 1 2
1 2 2 45
2 3 3 67
You can filter values
of dict
in dict comprehension
, then DataFrame
works perfectly:
print ({k:v[:min_length] for k,v in data_dict.items()})
{'b': [1, 2, 3], 'c': [2, 45, 67], 'a': [1, 2, 3]}
df = pd.DataFrame({k:v[:min_length] for k,v in data_dict.items()})
print (df)
a b c
0 1 1 2
1 2 2 45
2 3 3 67
If is possible some length can be less as min_length
add Series
:
data_dict = {'a': [1,2,3,4], 'b': [1,2], 'c': [2,45,67,93,82,92]}
min_length = 3
df = pd.DataFrame({k:pd.Series(v[:min_length]) for k,v in data_dict.items()})
print (df)
a b c
0 1 1.0 2
1 2 2.0 45
2 3 NaN 67
Timings :
In [355]: %timeit (pd.DataFrame({k:v[:min_length] for k,v in data_dict.items()}))
The slowest run took 5.32 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 520 µs per loop
In [356]: %timeit (pd.DataFrame({k:pd.Series(v[:min_length]) for k,v in data_dict.items()}))
The slowest run took 4.50 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 937 µs per loop
#Allen's solution
In [357]: %timeit (pd.DataFrame.from_dict(data_dict,orient='index').T.dropna())
1 loop, best of 3: 16.7 s per loop
Code for timings :
np.random.seed(123)
L = list('ABCDEFGH')
N = 500000
min_length = 10000
data_dict = {k:np.random.randint(10, size=np.random.randint(N)) for k in L}
A one-liner solution:
#Construct the df horizontally and then transpose. Finally drop rows with nan.
pd.DataFrame.from_dict(data_dict,orient='index').T.dropna()
Out[326]:
a b c
0 1.0 1.0 2.0
1 2.0 2.0 45.0
2 3.0 3.0 67.0
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