/ joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1475 3.3.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476 3.3.13 Accessors / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 2009 3.4.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2009 3.4.13 Flags The various time concepts supported by pandas are explained in the user guide section on time related concepts. I want to add a new column to the DataFrame containing only the month of the measurement

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 862 2.20.9 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 878 2.20.10 Resampling / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1453 3.3.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1454 3.3.13 Accessors / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1970 3.4.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1970 3.4.13 Flags

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 862 2.20.9 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 878 2.20.10 Resampling / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1453 3.3.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1454 3.3.13 Accessors / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1971 3.4.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1971 3.4.13 Flags

simultaneously. Matching / broadcasting behavior DataFrame has the methods add(), sub(), mul(), div() and related functions radd(), rsub(), for carrying out binary operations. For broadcasting behavior, Series input statistics There exists a large number of methods for computing descriptive statistics and other related operations on Series, DataFrame. Most of these are aggregations (hence producing a lower-dimensional feature; it can be used to easily link or map values defined by a secondary series. This is closely related to merging/joining functionality: In [192]: s = pd.Series(['six', 'seven', 'six', 'seven', 'six']

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 16.6 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 16.7 Up- D0 248 250 D1 252 254 [32 rows x 2 columns] Using a boolean indexer you can provide selection related to the values. In [59]: mask = df[(’a’,’foo’)]>200 In [60]: df.loc[idx[mask,:,[’C1’,’C3’]],idx[: Suggested tutorials in new Tutorials section. • Our pandas ecosystem is growing, We now feature related projects in a new Pandas Ecosystem section. • Much work has been taking place on improving the docs

0.0 Matching / broadcasting behavior DataFrame has the methods add(), sub(), mul(), div() and related functions radd(), rsub(), ... for carrying out binary operations. For broadcasting behavior, Series statistics There exists a large number of methods for computing descriptive statistics and other related operations on Series, DataFrame. Most of these are aggregations (hence producing a lower-dimensional feature; it can be used to easily “link” or “map” values defined by a secondary series. This is closely related to merging/joining functionality: In [197]: s = pd.Series(['six', 'seven', 'six', 'seven', 'six']

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 2.14.9 Time Series-Related Instance Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 2.14.10 Resampling merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217 3.3.12 Time series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218 3.3.13 Accessors merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1683 3.4.12 Time series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1683 3.4.13 Metadata

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742 2.14.9 Time Series-Related Instance Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 756 2.14.10 Resampling merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1221 3.3.12 Time series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1221 3.3.13 Accessors merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1686 3.4.12 Time series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1686 3.4.13 Metadata

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 2.17.9 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 768 2.17.10 Resampling / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1257 3.3.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1257 3.3.13 Accessors / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1750 3.4.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1750 3.4.13 Metadata

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 2.17.9 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 768 2.17.10 Resampling / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1257 3.3.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1257 3.3.13 Accessors / joining / merging . . . . . . . . . . . . . . . . . . . . . . . . . . 1750 3.4.12 Time Series-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1750 3.4.13 Metadata