to None. This is to preseve the original dtype unless explicitly requested otherwise (GH6290). • When converting a dataframe to HTML it used to return Empty DataFrame. This special case has been removed 471435 -1.190976 d 1.432707 -0.312652 b c -0.720589 0.887163 d 0.859588 -0.636524 This also applies when passing multiple indices to set_index: # Old output, 2-level MultiIndex of tuples In [14]: df_multi functions to Index for counting unique elements. (GH6734) • stack and unstack now raise a ValueError when the level keyword refers to a non-unique item in the Index (previously raised a KeyError). (GH6738)

apply will use the reduce argument to determine whether a Series or a DataFrame should be returned when the DataFrame is empty (GH6007). Previously, calling DataFrame.apply an empty DataFrame would return function being called with: Series([], dtype: float64) Out[33]: a NaN b NaN dtype: float64 Now, when apply is called on an empty DataFrame: if the reduce argument is True a Series will returned, if it parse_dates=True, infer_datetime_format=True) • date_format and datetime_format keywords can now be specified when writing to excel files (GH4133) 1.1. v0.13.1 (February 3, 2014) 7 pandas: powerful Python data analysis

files, Python 3 support for HDFStore, filtering of groupby expressions via filter, and a revamped replace routine that accepts regular expressions. 1.1.1 API changes • The I/O API is now much more consistent 12.0 In [9]: df2.groupby("val1").apply(func) 0 1 2 3 val1 1 0.5 -0.5 7.5 -7.5 • Raise on iloc when boolean indexing with a label based indexer mask e.g. a boolean Series, even with integer labels, raise_on_error argument to plotting functions is removed. Instead, plotting functions raise a TypeError when the dtype of the object is object to remind you to avoid object arrays whenever possible and thus

conflicting attribute/column names now behave consistently between getting and setting. Previously, when both a column and attribute named y existed, data.y would return the attribute, while data.y = z would Categorical class (GH8420). Other enhancements: • Added the ability to specify the SQL type of columns when writing a DataFrame to a database (GH8778). For example, specifying to use the sqlalchemy String type columns that contain NA values and have dtype object (GH8778). 1.1.3 Performance • Reduce memory usage when skiprows is an integer in read_csv (GH8681) • Performance boost for to_datetime conversions with

Yahoo Options page (GH8612), (GH8741) Note: As a result of a change in Yahoo’s option page layout, when an expiry date is given, Options methods now return data for a single expiry date. Previously, methods properties is_monotonic_increasing and is_monotonic_decreasing (GH8680). • Added option to select columns when importing Stata files (GH7935) • Qualify memory usage in DataFrame.info() by adding + if it is a All countries will work now, but some bad countries will raise exceptions because some edge cases break the entire response. (GH8482) • Added option to Series.str.split() to return a DataFrame rather than

Check the advanced installation page. Learn more 1.2 Intro to pandas Straight to tutorial... When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: source activate name_of_my_env On Windows the command is: activate name_of_my_env You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies pandas has many optional dependencies that are

Check the advanced installation page. Learn more 1.2 Intro to pandas Straight to tutorial... When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies pandas has many optional dependencies that are

Check the advanced installation page. Learn more 1.2 Intro to pandas Straight to tutorial... When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies pandas has many optional dependencies that are

Check the advanced installation page. Learn more 1.2 Intro to pandas Straight to tutorial... When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies pandas has many optional dependencies that are

Check the advanced installation page. Learn more 1.2 Intro to pandas Straight to tutorial... When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool python This will create a minimal environment with only Python installed in it. To put your self inside this environment run: 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets. Optional dependencies pandas has many optional dependencies that are