NaN S [5 rows x 12 columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain the output in more detail: • functions. • Exporting data out of pandas is provided by different to_*methods. • The head/tail/info methods and the dtypes attribute are convenient for a first check. For a complete overview of the

NaN S [5 rows x 12 columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain the output in more detail: • functions. • Exporting data out of pandas is provided by different to_*methods. • The head/tail/info methods and the dtypes attribute are convenient for a first check. For a complete overview of the

35.0 0 0 373450 8.0500 NaN S I’m interested in a technical summary of a DataFrame In [9]: titanic.info() RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain the output in more detail: • functions. • Exporting data out of pandas is provided by different to_*methods. • The head/tail/info methods and the dtypes attribute are convenient for a first check. For a complete overview of the

NaN S [5 rows x 12 columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain the output in more detail: • functions. • Exporting data out of pandas is provided by different to_*methods. • The head/tail/info methods and the dtypes attribute are convenient for a first check. For a complete overview of the

NaN S [5 rows x 12 columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain the output in more detail: • functions. • Exporting data out of pandas is provided by different to_*methods. • The head/tail/info methods and the dtypes attribute are convenient for a first check. For a complete overview of the

warnings summary =============================== =========================== short test summary info ============================ = 1 failed, 146194 passed, 7402 skipped, 1367 xfailed, 5 xpassed, 197 NaN S [5 rows x 12 columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB The method info() provides technical information about a DataFrame, so let’s explain the output in more detail: •

option to select columns when importing Stata files (GH7935) • Qualify memory usage in DataFrame.info() by adding + if it is a lower bound (GH8578) • Raise errors in certain aggregation cases where an option to Series.str.split() to return a DataFrame rather than a Series (GH8428) • Added option to df.info(null_counts=None|True|False) to override the default display options and force showing of the null-counts properties accessor .dt for Series, see Datetimelike Properties – New DataFrame default display for df.info() to include memory usage, see Memory Usage – read_csv will now by default ignore blank lines when

option to select columns when importing Stata files (GH7935) • Qualify memory usage in DataFrame.info() by adding + if it is a lower bound (GH8578) • Raise errors in certain aggregation cases where an option to Series.str.split() to return a DataFrame rather than a Series (GH8428) • Added option to df.info(null_counts=None|True|False) to override the default display options and force showing of the null-counts properties accessor .dt for Series, see Datetimelike Properties – New DataFrame default display for df.info() to include memory usage, see Memory Usage – read_csv will now by default ignore blank lines when

contains NaT. (GH10388) • Bug in ExcelReader when worksheet is empty (GH6403) • Bug in BinGrouper.group_info where returned values are not compatible with base class (GH10914) • Bug in clearing the cache on format as DatetimeIndex. (GH9116) • Bar and horizontal bar plots no longer add a dashed line along the info axis. The prior style can be achieved with matplotlib’s axhline or axvline methods (GH9088). • Series GH6620) • Fixed bug in to_sql when mapping a Timestamp object column (datetime column with timezone info) to the appropriate sqlalchemy type (GH9085). • Fixed bug in to_sql dtype argument not accepting

showing the class name with the large_repr set to ‘info’ (GH7105) • The verbose keyword in DataFrame.info(), which controls whether to shorten the info representation, is now None by default. This will setting in display.max_info_columns. The global setting can be overriden with verbose=True or verbose=False. • Fixed a bug with the info repr not honoring the display.max_info_columns setting (GH6939) (GH6939) • Offset/freq info now in Timestamp __repr__ (GH4553) 1.1.3 Text Parsing API Changes read_csv()/read_table() will now be noiser w.r.t invalid options rather than falling back to the PythonParser.