. . . . . . . . . . . . . . 9 1.2.1.4 drop now also accepts index/columns keywords . . . . . . . . . . . . . . . . . . . 10 1.2.1.5 rename, reindex now also accept axis keyword . . . . . . . . . . CategoricalDtype for specifying categoricals . . . . . . . . . . . . . . . . . 11 1.2.1.7 GroupBy objects now have a pipe method . . . . . . . . . . . . . . . . . . . . . 12 1.2.1.8 Categorical.rename_categories . . . . . . . . . . . . . . . . . . 15 1.2.2.2 Sum/Prod of all-NaN or empty Series/DataFrames is now consistently NaN . . . . 15 1.2.2.3 Indexing with a list with missing labels is Deprecated . . .

support for compressed URLs in read_csv . . . . . . . . . . . . . . . . . 13 1.3.1.6 Pickle file I/O now supports compression . . . . . . . . . . . . . . . . . . . . . . . 13 1.3.1.7 UInt64 Support Improved types now return other Index types . . . . . . . . . . . . . . . . . . 23 1.3.2.3 Accessing datetime fields of Index now return Index . . . . . . . . . . . . . . . . . 24 1.3.2.4 pd.unique will now be consistent . . . . . . . . . . . . . . . . . . . . . . . . . 32 1.3.2.14 Index.intersection and inner join now preserve the order of the left Index . . . . . . 32 1.3.2.15 Pivot Table always returns a DataFrame

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 types now return other Index types . . . . . . . . . . . . . . . . . . 21 1.2.2.3 Accessing datetime fields of Index now return Index . . . . . . . . . . . . . . . . . 23 1.2.2.4 pd.unique will now be consistent . . . . . . . . . . . . . . . . . . . . . . . . . 30 1.2.2.14 Index.intersection and inner join now preserve the order of the left Index . . . . . . 31 1.2.2.15 Pivot Table always returns a DataFrame

randn(4)}) Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. 1.4. Tutorials 81 pandas: powerful Python data analysis 141 pandas: powerful Python data analysis toolkit, Release 1.3.4 (continued from previous page) * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left ["Chicago, IL"]}) extract_city_name and add_country_name are functions taking and returning DataFrames. Now compare the following: In [145]: add_country_name(extract_city_name(df_p), country_name="US") Out[145]:

randn(4)} ˓→) Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key create df2 and save to disk clear input str1 key B D D E end generate value = rnormal() save df2.dta * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left ["Chicago, IL"]}) extract_city_name and add_country_name are functions taking and returning DataFrames. Now compare the following: In [145]: add_country_name(extract_city_name(df_p), country_name="US") Out[145]:

randn(4)}) Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key 141 pandas: powerful Python data analysis toolkit, Release 1.3.3 (continued from previous page) * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left ["Chicago, IL"]}) extract_city_name and add_country_name are functions taking and returning DataFrames. Now compare the following: In [145]: add_country_name(extract_city_name(df_p), country_name="US") Out[145]:

missing values A new pd.NA value (singleton) is introduced to represent scalar missing values. Up to now, pandas used several values to represent missing data: np.nan is used for this for float data, np.nan (GH27292) • DataFrame.to_latex() now accepts caption and label arguments (GH25436) • DataFrames with nullable integer, the new string dtype and period data type can now be converted to pyarrow (>=0.15 >= 0.16 (GH20612). • to_parquet() now appropriately handles the schema argument for user defined schemas in the pyarrow engine. (GH30270) • DataFrame.to_json() now accepts an indent integer argument

randn(4)}) ....: Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key create df2 and save to disk clear input str1 key B D D E end generate value = rnormal() save df2.dta * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left 2000-01-08 NaN NaN NaN ˓→ NaN ... NaN NaN NaN NaN [8 rows x 11 columns] Warning: df - df['A'] is now deprecated and will be removed in a future release. The preferred way to replicate this behavior is

randn(4)}) ....: Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key create df2 and save to disk clear input str1 key B D D E end generate value = rnormal() save df2.dta * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left NaN NaN ˓→ NaN NaN ... NaN NaN NaN ˓→NaN NaN [8 rows x 11 columns] Warning: df - df['A'] is now deprecated and will be removed in a future release. The preferred way to replicate this behavior is

randn(4)}) Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key 141 pandas: powerful Python data analysis toolkit, Release 1.4.4 (continued from previous page) * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left ["Chicago, IL"]}) extract_city_name and add_country_name are functions taking and returning DataFrames. Now compare the following: In [145]: add_country_name(extract_city_name(df_p), country_name="US") Out[145]: