top-level pandas.* namespace, please see the changes here. Check the API Changes and deprecations before updating. Note: This is a combined release for 0.20.0 and and 0.20.1. Version 0.20.1 contains one infer- ence (GH11256) • Bug in pd.read_csv() in which the dialect parameter was not being verified before processing (GH14898) • Bug in pd.read_csv() in which missing data was being improperly handled with quotes and we want to asof-join them. This also illustrates using the by parameter to group data before merging. In [7]: trades = pd.DataFrame({ ...: 'time': pd.to_datetime(['20160525 13:30:00.023',

quotes and we want to asof-join them. This also illustrates using the by parameter to group data before merging. In [7]: trades = pd.DataFrame({ ...: 'time': pd.to_datetime(['20160525 13:30:00.023', are using a grouper in the by field. This is like a left-outer join, except that forward filling happens automatically taking the most recent non-NaN value. In [11]: pd.merge_asof(trades, quotes, ... integer line_width and index=False raises an Unbound- LocalError exception because idx referenced before assignment. • Bug in eval() where the resolvers argument would not accept a list (GH14095) • Bugs

quotes and we want to asof-join them. This also illustrates using the by parameter to group data before merging. In [7]: trades = pd.DataFrame({ ...: 'time': pd.to_datetime(['20160525 13:30:00.023', are using a grouper in the by field. This is like a left-outer join, except that forward filling happens automatically taking the most recent non-NaN value. In [11]: pd.merge_asof(trades, quotes, ... integer line_width and index=False raises an Unbound- LocalError exception because idx referenced before assignment. • Bug in eval() where the resolvers argument would not accept a list (GH14095) • Bugs

top-level pandas.* namespace, please see the changes here. Check the API Changes and deprecations before updating. Note: This is a combined release for 0.20.0 and and 0.20.1. Version 0.20.1 contains one infer- ence (GH11256) • Bug in pd.read_csv() in which the dialect parameter was not being verified before processing (GH14898) • Bug in pd.read_csv() in which missing data was being improperly handled with quotes and we want to asof-join them. This also illustrates using the by parameter to group data before merging. 52 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.20.2

(GH11097) • Compatibility with matplotlib 1.5.0 (GH11111) Check the API Changes and deprecations before updating. What’s new in v0.17.0 • New features – Datetime with TZ – Releasing the GIL – Plot submethods SettingWithCopyWarning (GH10738) • Bug in .sample() where weights passed as Series were not aligned along axis before being treated posi- tionally, potentially causing problems if weight indices were not aligned with pandas: powerful Python data analysis toolkit, Release 0.17.0 Check the API Changes and deprecations before updating. What’s new in v0.16.0 • New features – DataFrame Assign – Interaction with scipy.sparse

the selection brackets []. Only rows for which the value is True will be selected. We know from before that the original Titanic DataFrame consists of 891 rows. Let’s have a look at the number of rows loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. condition expression, use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional

the selection brackets []. Only rows for which the value is True will be selected. We know from before that the original Titanic DataFrame consists of 891 rows. Let’s have a look at the number of rows loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. condition expression, use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional

the selection brackets []. Only rows for which the value is True will be selected. We know from before that the original Titanic DataFrame consists of 891 rows. Let’s have a look at the number of rows loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. condition expression, use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional

the selection brackets []. Only rows for which the value is True will be selected. We know from before that the original Titanic DataFrame consists of 891 rows. Let’s have a look at the number of rows 26 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.3.2 part before the comma is the rows you want, and the part after the comma is the columns you want to select. condition expression, use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional

the selection brackets []. Only rows for which the value is True will be selected. We know from before that the original Titanic DataFrame consists of 891 rows. Let’s have a look at the number of rows loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. condition expression, use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional