checked out using git and compiled / installed like so: git clone git://github.com/pydata/pandas.git cd pandas python setup.py install On Windows, I suggest installing the MinGW compiler suite following ummary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- GOOG 0 ummary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- GOOG 0

checked out using git and compiled / installed like so: git clone git://github.com/pydata/pandas.git cd pandas python setup.py install On Windows, I suggest installing the MinGW compiler suite following ummary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- GOOG 0 ummary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- GOOG 0

in raw data. That said, you may want to avoid introducing duplicates as part of a data processing pipeline (from methods like pandas. concat(), rename(), etc.). Both Series and DataFrame disallow duplicate duplicate labels), deduplicate, and then disallow duplicates going forward, to ensure that your data pipeline doesn’t introduce duplicates. >>> raw = pd.read_csv("...") >>> deduplicated = raw.groupby(level=0) ----------------------------------- NameError Traceback (most recent call last) cd9ac77fc4c4> in ----> 1 data = pd.Series(np.random.randn(1000)) NameError: name 'pd' is not

in raw data. That said, you may want to avoid introducing duplicates as part of a data processing pipeline (from methods like pandas. concat(), rename(), etc.). Both Series and DataFrame disallow duplicate duplicate labels), deduplicate, and then disallow duplicates going forward, to ensure that your data pipeline doesn’t introduce duplicates. >>> raw = pd.read_csv("...") >>> deduplicated = raw.groupby(level=0) In [127]: plt.figure(); In [128]: ax = df.plot(secondary_y=["A", "B"]) In [129]: ax.set_ylabel("CD scale"); In [130]: ax.right_ax.set_ylabel("AB scale"); 2.15. Chart Visualization 695 pandas: powerful

Contributing section has been added. • Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI. Warning: 0.13.1 fixes a bug that was caused by a combination of having numpy clone your fork to your machine: git clone git@github.com:your-user-name/pandas.git pandas-yourname cd pandas-yourname git remote add upstream git://github.com/pydata/pandas.git This creates the directory run automatically on Travis-CI once your Pull Request is submitted. However, if you wish to run the test suite on a branch prior to submitting the Pull Request, then Travis-CI needs to be hooked up to your

Contributing section has been added. • Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI. Warning: 0.13.1 fixes a bug that was caused by a combination of having numpy your fork to your machine: git clone https://github.com/your-user-name/pandas.git pandas-yourname cd pandas-yourname git remote add upstream https://github.com/pandas-dev/pandas.git This creates the sure your conda is up to date (conda update conda) • Make sure that you have cloned the repository • cd to the pandas source directory We’ll now kick off a three-step process: 1. Install the build dependencies

AxesSubplot object at 0x7f19f2847490>, cd0>, , , cd0050>, , In [124]: ax = df.plot(secondary_y=['A', 'B']) In [125]: ax.set_ylabel('CD scale') Out[125]: Text(0, 0.5, 'CD scale') In [126]: ax.right_ax.set_ylabel('AB scale') \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[126]:

Contributing section has been added. • Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI. Warning: 0.13.1 fixes a bug that was caused by a combination of having numpy clone your fork to your machine: git clone git@github.com:your-user-name/pandas.git pandas-yourname cd pandas-yourname git remote add upstream git://github.com/pydata/pandas.git This creates the directory run automatically on Travis-CI once your pull request is submitted. However, if you wish to run the test suite on a branch prior to submitting the pull request, then Travis-CI needs to be hooked up to your

Contributing section has been added. • Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI. Warning: 0.13.1 fixes a bug that was caused by a combination of having numpy clone your fork to your machine: git clone git@github.com:your-user-name/pandas.git pandas-yourname cd pandas-yourname git remote add upstream git://github.com/pandas-dev/pandas.git This creates the directory run automatically on Travis-CI once your pull request is submitted. However, if you wish to run the test suite on a branch prior to submitting the pull request, then Travis-CI needs to be hooked up to your

in raw data. That said, you may want to avoid introducing duplicates as part of a data processing pipeline (from methods like pandas. concat(), rename(), etc.). Both Series and DataFrame disallow duplicate duplicate labels), deduplicate, and then disallow duplicates going forward, to ensure that your data pipeline doesn’t introduce duplicates. >>> raw = pd.read_csv("...") >>> deduplicated = raw.groupby(level=0) In [125]: plt.figure(); In [126]: ax = df.plot(secondary_y=["A", "B"]) In [127]: ax.set_ylabel("CD scale"); In [128]: ax.right_ax.set_ylabel("AB scale"); 652 Chapter 2. User Guide pandas: powerful