Dependency Minimum Version Notes fsspec 2021.5.0 Handling files aside from simple local and HTTP gcsfs 2021.5.0 Google Cloud Storage access pandas-gbq 0.15.0 Google Big Query access s3fs 2021.05.0 Amazon S3 is a CSV file. In Excel, you would download and then open the CSV. In pandas, you pass the URL or local path of the CSV file to read_csv(): In [5]: url = ( ...: "https://raw.github.com/pandas-dev" . ["filename_01", "filename_02"], .....: "path": [ .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [131]: pd.set_option("display

Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google Big Query access s3fs 0.4.0 Amazon S3 access is a CSV file. In Excel, you would download and then open the CSV. In pandas, you pass the URL or local path of the CSV file to read_csv(): In [5]: url = ( ...: "https://raw.github.com/pandas-dev" . ["filename_01", "filename_02"], .....: "path": [ .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [128]: pd.set_option("display

Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google Big Query access s3fs 0.4.0 Amazon S3 access is a CSV file. In Excel, you would download and then open the CSV. In pandas, you pass the URL or local path of the CSV file to read_csv(): In [5]: url = ( ...: "https://raw.github.com/pandas-dev" . ["filename_01", "filename_02"], .....: "path": [ .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [128]: pd.set_option("display

Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.14.0 Google Big Query access s3fs 0.4.0 Amazon S3 access is a CSV file. In Excel, you would download and then open the CSV. In pandas, you pass the URL or local path of the CSV file to read_csv(): In [5]: url = ( ...: "https://raw.github.com/pandas-dev" . ["filename_01", "filename_02"], .....: "path": [ .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [128]: pd.set_option("display

Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.14.0 Google Big Query access s3fs 0.4.0 Amazon S3 access is a CSV file. In Excel, you would download and then open the CSV. In pandas, you pass the URL or local path of the CSV file to read_csv(): In [5]: url = ( ...: "https://raw.github.com/pandas-dev" . ["filename_01", "filename_02"], .....: "path": [ .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [128]: pd.set_option("display

Dependency Minimum Version Notes fsspec 0.7.4 Handling files aside from simple local and HTTP gcsfs 0.6.0 Google Cloud Storage access pandas-gbq 0.12.0 Google Big Query access s3fs 0.4.0 Amazon S3 access is a CSV file. In Excel, you would download and then open the CSV. In pandas, you pass the URL or local path of the CSV file to read_csv(): In [5]: url = ( ...: "https://raw.github.com/pandas-dev" . Release 1.3.2 (continued from previous page) .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [128]: pd.set_option("display

Compression for HDF5 fsspec 0.7.4 Handling files aside from local and HTTP fastparquet 0.4.0 Parquet reading / writing gcsfs 0.6.0 Google Cloud Storage access html5lib 1.0.1 HTML parser for read_html (see Release 1.2.3 (continued from previous page) .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [128]: pd.set_option("display DataFrame(datafile) Out[129]: filename path 0 filename_01 media/user_name/storage/fo... 1 filename_02 media/user_name/storage/fo... In [130]: pd.set_option("display.max_colwidth", 100) In [131]: pd

Compression for HDF5 fsspec 0.7.4 Handling files aside from local and HTTP fastparquet 0.3.2 Parquet reading / writing gcsfs 0.6.0 Google Cloud Storage access html5lib 1.0.1 HTML parser for read_html (see Release 1.2.0 (continued from previous page) .....: "media/user_name/storage/folder_01/filename_01", .....: "media/user_name/storage/folder_02/filename_02", .....: ], .....: } .....: In [128]: pd.set_option("display DataFrame(datafile) Out[129]: filename path 0 filename_01 media/user_name/storage/fo... 1 filename_02 media/user_name/storage/fo... In [130]: pd.set_option("display.max_colwidth", 100) In [131]: pd

droplevel() are now implemented (GH20342) • Added support for reading from/writing to Google Cloud Storage via the gcsfs library (GH19454, GH23094) • DataFrame.to_gbq() and read_gbq() signature and documentation constructor (GH2193) • DatetimeIndex has gained the DatetimeIndex.timetz attribute. This returns the local time with timezone information. (GH21358) 12 Chapter 1. What’s New in 0.24.0 (January 25, 2019) xlrd 1.0.0 pytest (dev) 3.6 Additionally we no longer depend on feather-format for feather based storage and replaced it with references to pyarrow (GH21639 and GH23053). 1.2.2 os.linesep is used for line_terminator

DataFrame.eval method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152 26.3.4 Local Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154 terminal size. This fix only applies to python 3 (GH16496) • Bug in using pathlib.Path or py.path.local objects with io functions (GH16291) • Bug in Index.symmetric_difference() on two equal MultiIndex’s cause overflow on win- dows (GH15265) • Bug in .eval() which caused multiline evals to fail with local variables not on the first line (GH15342) 1.5.7.9 Other • Compat with SciPy 0.19.0 for testing on