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 1.4. Tutorials 11 ["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 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

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 1.4. Tutorials 11 ["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 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

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 Clipboard Dependency 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 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

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 access Clipboard Dependency ["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 DataFrame(datafile) Out[132]: filename path 0 filename_01 media/user_name/storage/fo... 1 filename_02 media/user_name/storage/fo... In [133]: pd.set_option("display.max_colwidth", 100) In [134]: pd

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 1.4. Tutorials 11 ["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 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

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 1.4. Tutorials 11 ["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 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

S3 (GH11070, GH11072). • In pd.read_csv, recognize s3n:// and s3a:// URLs as designating S3 file storage (GH11070, GH11071). • Read CSV files from AWS S3 incrementally, instead of first downloading the (GH11070, GH11073) • pd.read_csv is now able to infer compression type for files read from AWS S3 storage (GH11070, GH11074). 1.1. v0.17.0 (October 9, 2015) 15 pandas: powerful Python data analysis toolkit This is a container around a Categorical (introduced in v0.15.0) and allows efficient indexing and storage of an index with a large number of duplicated elements. Prior to 0.16.1, setting the index of a DataFrame/Series

lightweight portable binary format. See the docs Warning: Since this is an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release. In [118]: df = DataFrame(np.random.rand(5,2),columns=list(’AB’)) Release 0.14.0 • SciPy: miscellaneous statistical functions • PyTables: necessary for HDF5-based storage • SQLAlchemy: for SQL database support. Version 0.8.1 or higher recommended. • matplotlib: for this. • Chapter 5: Here you get to find out if it’s cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. • Chapter 6: Strings with pandas are great

lightweight portable binary format. See the docs Warning: Since this is an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release. 28 Chapter 1. What’s New pandas: powerful Python Release 0.13.1 • SciPy: miscellaneous statistical functions • PyTables: necessary for HDF5-based storage • matplotlib: for plotting • statsmodels – Needed for parts of pandas.stats • openpyxl, xlrd/xlwt this. • Chapter 5: Here you get to find out if it’s cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. • Chapter 6: Strings with pandas are great

argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) lightweight portable binary format. See the docs Warning: Since this is an EXPERIMENTAL LIBRARY, the storage format may not be stable until a future release. In [118]: df = DataFrame(np.random.rand(5,2),columns=list(’AB’)) 1 or higher. • SciPy: miscellaneous statistical functions • PyTables: necessary for HDF5-based storage. Version 3.0.0 or higher required. • SQLAlchemy: for SQL database support. Version 0.8.1 or higher