Pandas version checks

  • [X] I have checked that the issue still exists on the latest versions of the docs on main here

Location of the documentation

https://pandas.pydata.org/docs/user_guide/basics.html#dtypes

Documentation problem

In the dtypes section a list of integer types are given, but not floating point types. According to pandas.to_numeric() under the downcast argument, it appears float32 is the smallest floating point type, which implies float16 is not documented.

In addition, the integer types are listed with capitalization, but it appears that capitalization does not matter, which is not documented.

Suggested fix for documentation

  1. List supported floating point types.
  2. Mention float16 not supported. (Could be implicitly solved by point 1.)
  3. Mention entries are not case-sensitive.
  4. Link to NumPy types.

Resulting from https://github.com/pandas-dev/pandas/issues/50213.

Comment From: phofl

Capitalization matters. Int64 is an extension dtype while int64 is the numpy dtype. But this is documented

Comment From: mcp292

Thank you for clarifying. Can you please link to documentation?

Comment From: phofl

The table listing all int dtypes in your link already has a link to nullable integer. One of the first sentences there clarifies the capital I at the beginning

Comment From: MarcoGorelli

Do we just need to add a row with FloatArray then? I think this'd be fine - do you want to open a pull request? Here's the contributing guide https://pandas.pydata.org/docs/dev/development/contributing.html

Comment From: mcp292

The table listing all int dtypes in your link already has a link to nullable integer. One of the first sentences there clarifies the capital I at the beginning

I'm still not seeing the sentence you refer to. Can you please quote it?

Comment From: mcp292

Do we just need to add a row with FloatArray then? I think this'd be fine - do you want to open a pull request? Here's the contributing guide https://pandas.pydata.org/docs/dev/development/contributing.html

Sure! It'll be a few days before I can make time for this. Thank you for the link.

Comment From: MarcoGorelli

I'm still not seeing the sentence you refer to. Can you please quote it?

It's probably this one

Or the string alias "Int64" (note the capital "I", to differentiate from NumPy’s 'int64' dtype:

(btw, looks like there's a missing closed bracket)

Comment From: mcp292

Thank you! Would it be acceptable to add the closing parenthesis in the same PR?

Comment From: MarcoGorelli

yup 😄

Comment From: mcp292

Looks like there's no entry for FloatingArray.

I'm trying to track down a list of the available FloatingArray types. I'm looking first to track down the IntegerArray types listed on the dtypes page, so that I may find the same for FloatingArray.

Do you know where I can find this documentation?

Comment From: mcp292

I noticed that UInt8 warns against information loss due to conversion to a shorter type, but uint8 does not:

import pandas as pd


data = [255, 256, 257, 258, 259]
df = pd.DataFrame(data)

df.astype("uint8")
df.astype("UInt8")

This advantage is not documented on the nullable integer page. The only advantage listed there is the preservation of integer type in the presence of NaNs.

Is this worth a separate issue/PR?

Comment From: natmokval

take

Comment From: mcp292

take

Thank you, I've been too busy to address this.

Comment From: vijaybirju

Is this issue fixed?

Comment From: MarcoGorelli

hey - there's already a PR open, could you find another issue to work on please?

Comment From: vijaybirju

Ok thanks so in one issue only one person can do pull requests at a single time