the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: proc print data=df(obs=5); run; 3.5. Comparison the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) – the equivalent in Stata would be: list in 1/5 Data structures General terminology analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog

the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: 3.5. Comparison with other tools 181 pandas: the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) – the equivalent in Stata would be: list in 1/5 Data structures General terminology analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog

and can help reduce “boilerplate” when using DataFrame styling functionality repeatedly within a notebook. (GH23229) In [46]: df = pd.DataFrame({'N': [1250, 1500, 1750], 'X': [0.25, 0.35, 0.50]}) In [47]: the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: proc print data=df(obs=5); run; Data Structures the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) – the equivalent in Stata would be: list in 1/5 Data Structures General Terminology

'kde', 'line', 'pie', 'scatter'] Note: In many development environments as well as IPython and Jupyter Notebook, use the TAB button to get an 1.4. Tutorials 31 pandas: powerful Python data analysis toolkit analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog date_range("1/1/2000",␣ ˓→periods=1000)) In [134]: ts = ts.cumsum() In [135]: ts.plot(); If running under Jupyter Notebook, the plot will appear on plot(). Otherwise use matplotlib.pyplot.show to show it or matplotlib

'kde', 'line', 'pie', 'scatter'] Note: In many development environments as well as IPython and Jupyter Notebook, use the TAB button to get an 1.4. Tutorials 31 pandas: powerful Python data analysis toolkit analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog User Guide pandas: powerful Python data analysis toolkit, Release 1.4.2 If running under Jupyter Notebook, the plot will appear on plot(). Otherwise use matplotlib.pyplot.show to show it or matplotlib

'kde', 'line', 'pie', 'scatter'] Note: In many development environments as well as IPython and Jupyter Notebook, use the TAB button to get an 32 Chapter 1. Getting started pandas: powerful Python data analysis analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog is a standard python input, while in the second the In [1]: indicates the input is inside a notebook. In Jupyter Notebooks the last line is printed and plots are shown inline. For example: In [3]: a =

the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: proc print data=df(obs=5); run; 188 Chapter the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) – the equivalent in Stata would be: list in 1/5 Data structures General terminology analysis in Python with pandas (2016-2018) GitHub repo and Jupyter Notebook • Best practices with pandas (2018) GitHub repo and Jupyter Notebook Various tutorials • Wes McKinney’s (pandas BDFL) blog

uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here • Experimental support for exporting styled DataFrames (DataFrame.style) to Excel are using IPython (or another frontend like nteract using the Jupyter messaging protocol). This gives frontends like the Jupyter notebook and nteract more flexiblity in how they display pandas objects extension, see the example notebook (GH15649) • Styler.render() now accepts **kwargs to allow user-defined variables in the template (GH15649) • Compatibility with Jupyter notebook 5.0; MultiIndex column

uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here • Experimental support for exporting styled DataFrames (DataFrame.style) to Excel are using IPython (or another frontend like nteract using the Jupyter messaging protocol). This gives frontends like the Jupyter notebook and nteract more flexiblity in how they display pandas objects extension, see the example notebook (GH15649) • Styler.render() now accepts **kwargs to allow user-defined variables in the template (GH15649) • Compatibility with Jupyter notebook 5.0; MultiIndex column

'kde', 'line', 'pie', 'scatter'] Note: In many development environments as well as ipython and jupyter notebook, use the TAB button to get an overview of the available methods, for example air_quality.plot the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: proc print data=df(obs=5); run; Data structures the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) – the equivalent in Stata would be: list in 1/5 Data structures General terminology