fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be a string name of such an object (ie, ‘jet’). The colormap is sampled to select the color for each column. Please see Colormaps for more information. (GH3860) • DataFrame.interpolate() is now deprecated by default. (GH3907) • Add the keyword allow_duplicates to DataFrame.insert to allow a duplicate column to be inserted if True, default is False (same as prior to 0.12) (GH3679) 1.1. v0.12.0 (July 24

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures is as float32 D int32 E category F object dtype: object If youre using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Heres a subset of the attributes that

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be rows x 1 columns] 1.1.1 Output Formatting Enhancements • df.info() view now display dtype info per column (GH5682) • df.info() now honors the option max_info_rows, to disable null counts for large frames some cases this can increase the parsing speed by ~5-10x. # Try to infer the format for the index column df = pd.read_csv(’foo.csv’, index_col=0, parse_dates=True, infer_datetime_format=True) • date_format

data that can hold missing values. The default bool data type based on a bool-dtype NumPy array, the column can only hold True or False, and not missing values. This new BooleanArray can store missing values 1}, axis=1) Out[32]: 1 0 1 [1 rows x 1 columns] If you would like to update both the index and column labels, be sure to use the respective keywords. In [33]: df.rename(index={0: 1}, columns={0: 2}) RangeIndex: 3 entries, 0 to 2 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 int_col 3 non-null int64 1 text_col 3 non-null

agg( ...: min_height=pd.NamedAgg(column='height', aggfunc='min'), ...: max_height=pd.NamedAgg(column='height', aggfunc='max'), ...: average_weight=pd.NamedAgg(column='weight', aggfunc=np.mean), ...: names as the **kwargs to .agg. The values of **kwargs should be tuples where the first element is the column selection, and the second element is the aggregation function to apply. Pandas provides the pandas is the recommended replacement for the deprecated “dict-of-dicts” approach to naming the output of column-specific aggregations (Deprecate groupby.agg() with a dictionary when renaming). A similar approach

agg( ...: min_height=pd.NamedAgg(column='height', aggfunc='min'), ...: max_height=pd.NamedAgg(column='height', aggfunc='max'), ...: average_weight=pd.NamedAgg(column='weight', aggfunc=np.mean), ...: names as the **kwargs to .agg. The values of **kwargs should be tuples where the first element is the column selection, and the second element is the aggregation function to apply. Pandas provides the pandas is the recommended replacement for the deprecated “dict-of-dicts” approach to naming the output of column-specific aggregations (Deprecate groupby.agg() with a dictionary when renaming). A similar approach

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be it used to return Empty DataFrame. This special case has been removed, instead a header with the column names is returned (GH6062). • Series and Index now internall share more common operations, e.g. have changed – Column names are now given precedence over locals – Local variables must be referred to explicitly. This means that even if you have a local variable that is not a column you must still

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 2.9 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are pro- vided to combine multiple tables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 2.8.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 2.9 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are pro- vided to combine multiple tables

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486 2.5.11 Exploding a list-like column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 2.6 Working with text data loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward. To introduction introduction tutorial To user guide Straight to tutorial... Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are pro- vided to combine multiple tables