infer_dtype() function slower in latest version · Issue #28814 · pandas-dev/pandas (original) (raw)

Skip to content

Navigation Menu

• • GitHub Copilot Write better code with AI
  • GitHub Models New Manage and compare prompts
  • GitHub Advanced Security Find and fix vulnerabilities
  • Actions Automate any workflow
  • Codespaces Instant dev environments
  • Issues Plan and track work
  • Code Review Manage code changes
  • Discussions Collaborate outside of code
  • Code Search Find more, search less
• Explore
  • Learning Pathways
  • Events & Webinars
  • Ebooks & Whitepapers
  • Customer Stories
  • Partners
  • Executive Insights
• • GitHub Sponsors Fund open source developers
  • The ReadME Project GitHub community articles
• • Enterprise platform AI-powered developer platform
• Pricing

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Sign up

Appearance settings

Description

Code Sample

from datetime import datetime

import numpy as np import pandas as pd from pandas.api.types import infer_dtype

print(pd.version)

RUN_COUNT = 5

df = pd.DataFrame(np.ones((100000, 1000)))

avg_times = []

for _ in range(RUN_COUNT):

start = datetime.now()

for col in df.columns:
    infer_dtype(df[col], skipna=True)

avg_times.append(datetime.now() - start)

print('Average time: ', np.mean(avg_times))

Problem description

When I run the above code on my machine, there is major difference in the performance between different versions. I ran the code using new conda environments created with Python 3.7 and the specific version of Pandas. The table below shows the execution times.

The example code is only an example. In the most cases I need this function to get more specific type of the column than the type returned by the .dtype parameter (usually when working with mixed data).