asof-style time-series joining 6 .rolling() is now time-series aware 8 read_csv has improved support for duplicate column names 10 read_csv supports parsing Categorical directly 11 Categorical Concatenation returns Index 29 MultiIndex constructors, groupby and set_index preserve categorical dtypes 30 read_csv will progressively enumerate chunks 31 Sparse Changes 32 Indexer dtype changes 34 Other API numpy function compatibility ..... 55 Using .apply on groupby resampling ..... 55 Changes in read_csv exceptions ..... 56 to_datetime error changes ..... 57 Other API changes ..... 57 Deprecations

origin parameter 10 1.2.1.4 Groupby Enhancements 10 1.2.1.5 Better support for compressed URLs in read_csv 11 1.2.1.6 Pickle file I/O now supports compression 12 1.2.1.7 UInt64 Support Improved 13 1.2 joining 52 1.5.1.2 .rolling() is now time-series aware 54 1.5.1.3 read_csv has improved support for duplicate column names 56 1.5.1.4 read_csv supports parsing Categorical directly 56 1.5.1.5 Categorical 1.5.2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 75 1.5.2.12 read_csv will progressively enumerate chunks 77 1.5.2.13 Sparse Changes 78 1.5.2.14 Indexer dtype changes

time-series joining 5 .rolling() is now time-series aware 7 read_csv has improved support for duplicate column names 9 read_csv supports parsing Categorical directly 10 Categorical Concatenation Index 28 MultiIndex constructors, groupby and set_index preserve categorical dtypes 28 read_csv will progressively enumerate chunks 30 Sparse Changes 30 Indexer dtype changes 33 changes 52 numpy function compatibility 53 Using .apply on groupby resampling 54 Changes in read_csv exceptions 55 to_datetime error changes 56 Other API changes 56 Deprecations 57 1.2.4

top level reader functions accessed like pd.read_csv() that generally return a pandas object. - read_csv - read_excel - read_hdf - read_sql - read_json - read_html - read_stata - read_clipboard The corresponding files - The header option in read_csv now accepts a list of the rows from which to read the index. - The option, tupleize_cols can now be specified in both to_csv and read_csv, to provide compatibility for 119990 6 -0.460149 1.813962 7 -1.053571 0.009412 8 -0.165966 -0.848662 9 -0.495553 -0.176421 • read_csv will now throw a more informative error message when a file contains no columns, e.g., all newline

. . . . . . . . . . . . . . . . . . . . . . . 41 1.5.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 42 1.5.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . . 85 1.8.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 87 1.8.1.4 read_csv supports parsing Categorical directly . . . . . . 1.8.2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes106 1.8.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 108 1.8.2.13 Sparse Changes

. . . . . . . . . . . . . . . . . . . . . . . 12 1.3.1.5 Better support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 13 1.3.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . . . 56 1.6.1.3 read_csv has improved support for duplicate column names . . . . . . . . . . . 58 1.6.1.4 read_csv supports parsing Categorical directly . . . . . . 1.6.2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 77 1.6.2.12 read_csv will progressively enumerate chunks . . . . . . . . . . . . . . . . . . 79 1.6.2.13 Sparse Changes

available as a CSV file. In [2]: titanic = pd.read_csv("data/titanic.csv") pandas provides the read_csv() function to read data stored as a csv file into a pandas DataFrame. pandas supports many different 2019-05-07 06:00:00 NaN 61.9 NaN Note: The usage of the index_col and parse_dates parameters of the read_csv function to define the first (0th) column as index of the resulting DataFrame and convert the dates 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" ...: "/pandas/main/pandas/tests/io/data/csv/tips

all users upgrade to this version. Highlights include: • Added infer_datetime_format keyword to read_csv/to_datetime to allow speedups for homo- geneously formatted datetimes. • Will intelligently limit like inf (“inf”, “Inf”, “-Inf”, “iNf”, etc.) as infinity. (GH4220, GH4219), affecting read_table, read_csv, etc. • pandas now is Python 2/3 compatible without the need for 2to3 thanks to @jtratner. As a top level reader functions accessed like pd.read_csv() that generally return a pandas object. – read_csv – read_excel – read_hdf – read_sql – read_json – read_html – read_stata 1.3. v0.12.0 (July 24, 2013)

available as a CSV file. In [2]: titanic = pd.read_csv("data/titanic.csv") pandas provides the read_csv() function to read data stored as a csv file into a pandas DataFrame. pandas supports many different 2019-05-07 06:00:00 NaN 61.9 NaN Note: The usage of the index_col and parse_dates parameters of the read_csv function to define the first (0th) column as index of the resulting DataFrame and convert the dates data sets do contain datetime information in one of the columns, pandas input function like pandas. read_csv() and pandas.read_json() can do the transformation to dates when reading the data using the parse_dates

available as a CSV file. In [2]: titanic = pd.read_csv("data/titanic.csv") pandas provides the read_csv() function to read data stored as a csv file into a pandas DataFrame. pandas supports many different 2019-05-07 06:00:00 NaN 61.9 NaN Note: The usage of the index_col and parse_dates parameters of the read_csv function to define the first (0th) column as index of the resulting DataFrame and convert the dates 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" ...: "/pandas/master/pandas/tests/io/data/csv/tips