’slow’ is the longest string and there are no other strings with the same length ’w’ is the only non-null string in the yielded Series. • HDFStore – will retain index attributes (freq,tz,name) on recreation were was a relic of early pandas. This behavior can be re-enabled globally by the mode.use_inf_as_null option: In [11]: s = pd.Series([1.5, np.inf, 3.4, -np.inf]) In [12]: pd.isnull(s) 1.4. v0.10.0 set_option(’use_inf_as_null’, True) In [15]: pd.isnull(s) 0 False 1 True 2 False 3 True dtype: bool In [16]: s.fillna(0) 0 1.5 1 0.0 2 3.4 3 0.0 dtype: float64 In [17]: pd.reset_option(’use_inf_as_null’) •

id 100 non-null values year 100 non-null values stint 100 non-null values team 100 non-null values lg 100 non-null values g 100 non-null values ab 100 non-null values r 100 non-null values h 100 100 non-null values X2b 100 non-null values X3b 100 non-null values hr 100 non-null values rbi 100 non-null values sb 100 non-null values cs 100 non-null values bb 100 non-null values so 100 non-null non-null values ibb 100 non-null values hbp 100 non-null values sh 100 non-null values sf 100 non-null values gidp 100 non-null values dtypes: float64(9), int64(10), object(3) However, using to_string

id 100 non-null values year 100 non-null values stint 100 non-null values team 100 non-null values lg 100 non-null values g 100 non-null values ab 100 non-null values r 100 non-null values h 100 100 non-null values X2b 100 non-null values X3b 100 non-null values hr 100 non-null values rbi 100 non-null values sb 100 non-null values cs 100 non-null values bb 100 non-null values so 100 non-null non-null values ibb 100 non-null values hbp 100 non-null values sh 100 non-null values sf 100 non-null values gidp 100 non-null values dtypes: float64(9), int64(10), object(3) However, using to_string

id 100 non-null values year 100 non-null values stint 100 non-null values team 100 non-null values lg 100 non-null values g 100 non-null values ab 100 non-null values r 100 non-null values h 100 100 non-null values X2b 100 non-null values X3b 100 non-null values hr 100 non-null values rbi 100 non-null values sb 100 non-null values cs 100 non-null values bb 100 non-null values so 100 non-null non-null values ibb 100 non-null values hbp 100 non-null values sh 100 non-null values sf 100 non-null values gidp 100 non-null values dtypes: float64(9), int64(10), object(3) However, using to_string

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 (GH8701) 1.2.3 Bug Fixes • Bug in unpickling indexing with a list-like (GH8710) • Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722) • Bug in read_csv, dialect parameter would not take a string (:issue: 8703) • Bug Data columns (total 8 columns): bool 5000 non-null bool complex128 5000 non-null complex128 datetime64[ns] 5000 non-null datetime64[ns] float64 5000 non-null float64 1.3. v0.15.0 (October 18, 2014) 19

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 (GH8701) 1.1.3 Bug Fixes • Bug in unpickling indexing with a list-like (GH8710) • Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722) • Bug in read_csv, dialect parameter would not take a string (:issue: 8703) • Bug bool 5000 non-null bool complex128 5000 non-null complex128 datetime64[ns] 5000 non-null datetime64[ns] float64 5000 non-null float64 int64 5000 non-null int64 object 5000 non-null object timedelta64[ns]

Out[74]: 0 False 1 False 2 False dtype: bool Usually you simply want to know which values are null. In [75]: s.isnull() Out[75]: 0 False 1 True 2 False dtype: bool Warning: You generally will use isnull/notnull for these types of comparisons, as isnull/notnull tells you which elements are null. One has to be mindful that nan’s don’t compare equal, but None’s do. Note that Pandas/numpy uses length of index (GH11185) • Bug in convert_objects where converted values might not be returned if all null and coerce (GH9589) • Bug in convert_objects where copy keyword was not respected (GH9589) 1.2 v0

display dtype info per column (GH5682) • df.info() now honors the option max_info_rows, to disable null counts for large frames (GH5974) In [7]: max_info_rows = pd.get_option(’max_info_rows’) In [8]: date_range(’20130101’,periods=10))) ...: In [9]: df.iloc[3:6,[0,2]] = np.nan # set to not display the null counts In [10]: pd.set_option(’max_info_rows’,0) In [11]: df.info() DataFrame’> Int64Index: 10 entries, 0 to 9 Data columns (total 3 columns): A 7 non-null float64 B 10 non-null float64 C 7 non-null datetime64[ns] dtypes: datetime64[ns](1), float64(2) • Add show_dimensions

display dtype info per column (GH5682) • df.info() now honors the option max_info_rows, to disable null counts for large frames (GH5974) In [7]: max_info_rows = pd.get_option(’max_info_rows’) In [8]: date_range(’20130101’,periods=10))) ...: In [9]: df.iloc[3:6,[0,2]] = np.nan # set to not display the null counts In [10]: pd.set_option(’max_info_rows’,0) In [11]: df.info() 4 Chapter 1. What’s New pandas: DataFrame’> Int64Index: 10 entries, 0 to 9 Data columns (total 3 columns): A 7 non-null float64 B 10 non-null float64 C 7 non-null datetime64[ns] dtypes: datetime64[ns](1), float64(2) • Add show_dimensions

Bug in pd.read_csv() in the C engine where the NULL character was not being parsed as NULL (GH14012) • Bug in pd.read_csv() with engine='c' in which NULL quotechar was not accepted even though quoting rjust, and pad when passing non-integers, did not raise TypeError (GH13598) • Bug in checking for any null objects in a TimedeltaIndex, which always returned True (GH13603) • Bug in Series arithmetic raises [63]: pd.Timedelta('1s') / pd.NaT Out[63]: nan NaT may represent either a datetime64[ns] null or a timedelta64[ns] null. Given the ambiguity, it is treated as a timedelta64[ns], which allows more operations