从列表制作DataFrame的最快方法是什么？

Question

So basically im trying to transform a list into a DataFrame.

Here are the two ways of doing it that I am trying, but I cannot come up to a good performance benchmark.

import pandas as pd

mylist = [1,2,3,4,5,6]
names = ["name","name","name","name","name","name"]

# Way 1
pd.DataFrame([mylist], columns=names)

# Way 2
pd.DataFrame.from_records([mylist], columns=names)

I also tried dask but I did not find anything that could work for me.

Answer 1

so I just made up an example with 10 columns and random integers in the range of 1 Million values in those, i got the maximum result very quickly. Does this give you maybe a start to work with dask? They proposed the approach here which is also related to this question .

import dask.dataframe as dd
from dask.delayed import delayed
import pandas as pd
import numpy as np

# Create List with random integers
list_large = [np.random.random_sample(int(1e6))*i for i in range(10)]

# Convert it to dask dataframe
dfs = [delayed(pd.DataFrame)(i) for i in list_large]
df = dd.from_delayed(dfs)

# Calculate Maximum
max = df.max().compute()