Python | The fastest way to read CSV (csv/NumPy/pandas/VBA)

This article describes which is the fastest way to read CSV among csv (Python standard module) , NumPy, pandas, and Excel VBA.

Conclusion

pandas is the fastest. csv (Python standard module) is twice as slow. NumPy and VBA are 20x slower.

Experimental method

Measure the time 10 times to read a CSV file with Python standard module csv and put it in a variable. Measure this five times.
Same with NumPy
Same with pandas
Measure the time 10 times to read a CSV file with VBA and put it in a EXCEL. Measure this five times.

CSV file to read

200,000 rows of data 0,1,2,3,4,5,6,7,8,9
File size 4.0MB

This is what the file looks like when opened in Excel.

Code

Python standard module csv

#!/usr/bin/python

from time import time
import csv


file_name = r"C:\Users\yoshihiko\Desktop\yoshihiko.csv"
iteration = 10


def load_csv_BY_csv_reader(file):
    result = []
    for n in range(iteration):
        with open(file, "r", encoding="utf-8") as f:
            reader = csv.reader(f)
            for row in reader:
                result.append(row)
    return result

start_time = time()
result = load_csv_BY_csv_reader(file_name)
elapsed_time = time() - start_time
print("{:.3f}".format(elapsed_time), "秒")

NumPy

#!/usr/bin/python

from time import time
import numpy as np


file_name = r"C:\Users\yoshihiko\Desktop\yoshihiko.csv"
iteration = 10


def load_csv_BY_numpy(file):
    for n in range(iteration):
        csv = np.loadtxt(file, delimiter=',')
    return csv

start_time = time()
result = load_csv_BY_numpy(file_name)
elapsed_time = time() - start_time
print("{:.3f}".format(elapsed_time), "秒")

pandas

#!/usr/bin/python

from time import time
import pandas as pd


file_name = r"C:\Users\yoshihiko\Desktop\yoshihiko.csv"
iteration = 10


def load_csv_BY_pandas(file):
    for n in range(iteration):
        csv = pd.read_csv(file)
    return csv

start_time = time()
result = load_csv_BY_pandas(file_name)
elapsed_time = time() - start_time
print("{:.3f}".format(elapsed_time), "秒")

VBA

Option Explicit

Private Sub read_csv()
    Dim csv_path As String: csv_path = "C:\Users\yoshihiko\Desktop\yoshihiko.csv"
    Dim csv_file_name As String: csv_file_name = "yoshihiko.csv"
    Dim csv_sheet_name As String: csv_sheet_name = "yoshihiko"
    Dim self_file_name As String: self_file_name = Application.ActiveWorkbook.Name
    Dim i As Integer
    Dim iterate As Integer: iterate = 10
    Dim start_time As Variant
    Dim elapsed_time As Variant

    Application.ScreenUpdating = False
    start_time = Timer

    For i = 1 To iterate
        Workbooks(self_file_name).Activate
        Workbooks.Open csv_path
        Workbooks(self_file_name).Worksheets("Sheet1").Range("A1:J20000") = _
            Workbooks(csv_file_name).Worksheets(csv_sheet_name).Range("A1:J20000").Value
        Workbooks(csv_file_name).Close
    Next i

    elapsed_time = Timer - start_time
    Application.ScreenUpdating = True
    MsgBox elapsed_time & "sec"

End Sub

PC

CPU ： CORE i7 7th Gen ( i7-7500U 2.70GHz up to 3.50 GHz )

Result

pandas is the fastest with an average of 1.001 seconds.
Based on pandas, csv is 2x slower, NumPy and VBA 20x slower.

Trial Number	csv	NumPy	pandas	VBA
1	2.445	20.861	1.157	24.836
2	2.140	19.784	1.012	22.086
3	2.124	19.460	0.943	20.719
4	2.094	20.080	0.954	26.750
5	2.100	20.484	0.940	21.180
average	2.181	20.134	1.001	23.114