Version 1.0.0 of pandas, released on January 29, 2020, includes improvements to the dataframe.info method and introduces new data types for booleans and strings. Key enhancements also include performance gains by using the numba engine in rolling and expanding apply functions, along with the deprecation of numpy and datetime arguments in certain functions. Additionally, this version dropped support for Python 2, requiring at least Python 3.6 for use.

1. v1.0

2. What’s new in 1.0.0 (January 29, 2020)
● DataFrame.info method
improvement
● DataFrame.to_markdown

3. What’s new in 1.0.0 (January 29, 2020)
● New Data types for booleans and strings (previously object used for strings)
-But not added by default
-Change expected
● converted = df.convert_dtypes()
● df.select_dtypes("string")
● s = pd.Series([True, False, None], dtype="boolean") Out[15]:
● 0 True
● 1 False
● 2 <NA>

4. What’s new in 1.0.0 (January 29, 2020)
● DataFrame.rename now only accepts one positional argument
● The numpy argument of pandas.read_json() is deprecated
● Pandas.np
● pandas.datetime

5. What’s new in 1.0.0 (January 29, 2020)
● DataFrameGroupBy

6. What’s new in 1.0.0 (January 29, 2020)
Enhancements
Using Numba in rolling.apply and expanding.apply
We’ve added an engine keyword to apply() and apply() that allows the user to execute the routine using Numba instead of
Cython. Using the Numba engine can yield significant performance gains if the apply function can operate on numpy arrays and the
data set is larger (1 million rows or greater). For more details, see rolling apply documentation (GH28987, GH30936)
What’s Numba?
http://numba.pydata.org/

7. What’s new in 1.0.0 (January 29, 2020)
● This version of Pandas also drops support for Python 2. Using Pandas 1.0+ requires at least Python 3.6+, so make
sure your pip and python are using the correct versions.
pip install --upgrade pandas==1.0
https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.0.0.html