Pandas DataFrame performance

A dict is to a DataFrame as a bicycle is to a car.
You can pedal 10 feet on a bicycle faster than you can start a car, get it in gear, etc, etc. But if you need to go a mile, the car wins.

For certain small, targeted purposes, a dict may be faster.
And if that is all you need, then use a dict, for sure! But if you need/want the power and luxury of a DataFrame, then a dict is no substitute. It is meaningless to compare speed if the data structure does not first satisfy your needs.

Now for example — to be more concrete — a dict is good for accessing columns, but it is not so convenient for accessing rows.

import timeit

setup = '''
import numpy, pandas
df = pandas.DataFrame(numpy.zeros(shape=[10, 1000]))
dictionary = df.to_dict()

# f = ['value = dictionary[5][5]', 'value = df.loc[5, 5]', 'value = df.iloc[5, 5]']
f = ['value = [val[5] for col,val in dictionary.items()]', 'value = df.loc[5]', 'value = df.iloc[5]']

for func in f:
    print(min(timeit.Timer(func, setup).repeat(3, 100000)))


value = [val[5] for col,val in dictionary.iteritems()]
value = df.loc[5]
value = df.iloc[5]

So the dict of lists is 5 times slower at retrieving rows than df.iloc. The speed deficit becomes greater as the number of columns grows. (The number of columns is like the number of feet in the bicycle analogy. The longer the distance, the more convenient the car becomes…)

This is just one example of when a dict of lists would be less convenient/slower than a DataFrame.

Another example would be when you have a DatetimeIndex for the rows and wish to select all rows between certain dates. With a DataFrame you can use


There is no easy analogue for that if you were to use a dict of lists. And the Python loops you would need to use to select the right rows would again be terribly slow compared to the DataFrame.

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