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 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 can also disable this feature via the expand_frame_repr option. This will print the table in one block. 2.2. Intro to data structures 197 pandas: powerful Python data analysis toolkit, Release 1.3.3

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 can also disable this feature via the expand_frame_repr option. This will print the table in one block. 2.2. Intro to data structures 197 pandas: powerful Python data analysis toolkit, Release 1.3.4

“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 API. Support for non-unique indexes: In the latter case, you may have code inside a try:... catch: block that failed due to the index not being unique. In many cases it will no longer fail (some method like data analysis toolkit, Release 0.12.0 In [29]: pd.read_csv(StringIO(data), error_bad_lines=False) Skipping line 3: expected 3 fields, saw 4 Out[29]: a b c 0 1 2 3 1 8 9 10 18.1.14 Quoting and Escape

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 can also disable this feature via the expand_frame_repr option. This will print the table in one block. 2.2. Intro to data structures 197 pandas: powerful Python data analysis toolkit, Release 1.4.2

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 can also disable this feature via the expand_frame_repr option. This will print the table in one block. 2.2. Intro to data structures 197 pandas: powerful Python data analysis toolkit, Release 1.4.4

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 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 4 You can elect to skip bad lines: In [29]: pd.read_csv(StringIO(data), error_bad_lines=False) Skipping line 3: expected 3 fields, saw 4 Out[29]: a b c 0 1 2 3 1 8 9 10 You can also use the usecols

Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2536 4.10.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2536 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 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2530 4.10.5 Block manager rewrite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2530 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 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 import pandas as pd In [2]: pd.DataFrame({'A': [1, 2, 3]}) Out[2]: A 0 1 1 2 2 3 The first block is a standard python input, while in the second the In [1]: indicates the input is inside a notebook