“relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of analysis toolkit, Release 0.7.3 In [760]: axes[0].set_title(’Not interpolated’) Out[760]: In [761]: axes[1].set_title(’Interpolated’) Out[761]: title(’A’) Out[902]: In [903]: df[’B’].plot(ax=axes[0,1]); axes[0,1].set_title(’B’) Out[903]:

“relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of analysis toolkit, Release 0.7.1 In [744]: axes[0].set_title(’Not interpolated’) Out[744]: In [745]: axes[1].set_title(’Interpolated’) Out[745]: title(’A’) Out[885]: In [886]: df[’B’].plot(ax=axes[0,1]); axes[0,1].set_title(’B’) Out[886]:

“relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of analysis toolkit, Release 0.7.2 In [744]: axes[0].set_title(’Not interpolated’) Out[744]: In [745]: axes[1].set_title(’Interpolated’) Out[745]: title(’A’) Out[886]: In [887]: df[’B’].plot(ax=axes[0,1]); axes[0,1].set_title(’B’) Out[887]:

raised where arithmetic would broadcast ... ValueError: Invalid broadcasting comparison [(1, 2)] with block values In [8]: df + (1, 2) Out[8]: 0 1 0 1 3 1 3 5 2 5 7 In [9]: df == (1, 2, 3) ...: # length “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of can also disable this feature via the expand_frame_repr option. This will print the table in one block. DataFrame column attribute access and IPython completion If a DataFrame column label is a valid

Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2678 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2678 4 “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of for converting text to upper, lower, and title case, respectively. The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str. title(). In [40]: firstlast = pd.DataFrame({"string":

Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2758 4 “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of for converting text to upper, lower, and title case, respectively. The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str.title(). In [40]: firstlast = pd.DataFrame({"string":

Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2758 4 “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of for converting text to upper, lower, and title case, respectively. The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str.title(). In [40]: firstlast = pd.DataFrame({"string":

Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3020 4.11.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3021 4 “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of for converting text to upper, lower, and title case, respectively. The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str.title(). In [40]: firstlast = pd.DataFrame({"string":

Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2852 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2853 4 “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of for converting text to upper, lower, and title case, respectively. The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str.title(). In [40]: firstlast = pd.DataFrame({"string":

Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2850 4.13.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2851 4 “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of for converting text to upper, lower, and title case, respectively. The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str.title(). In [40]: firstlast = pd.DataFrame({"string":