financial applications. Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview DataFrame({'x': range(5), ....: 't': pd.date_range('2000-01-01', periods=5)}) ....: In [58]: df.reindex([0.1, 1.9, 3.5], ....: method='nearest', ....: tolerance=0.2) ....: Out[58]: t x 0.1 2000-01-01 0 1 Timedelta if possible. This allows you to specify tolerance with a string: In [59]: df = df.set_index('t') In [60]: df.reindex(pd.to_datetime(['1999-12-31']), ....: method='nearest', ....: tolerance='1

Anaconda distribution can be found here. Another advantage to installing Anaconda is that you don’t need admin rights to install it. Anaconda can install in the user’s home directory, which makes it trivial above. Handling ImportErrors If you encounter an ImportError, it usually means that Python couldn’t find pandas in the list of available libraries. Python internally has a list of directories it searches be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac you can run which

Anaconda distribution can be found here. Another advantage to installing Anaconda is that you don’t need admin rights to install it. Anaconda can install in the user’s home directory, which makes it trivial above. Handling ImportErrors If you encounter an ImportError, it usually means that Python couldn’t find pandas in the list of available libraries. Python internally has a list of directories it searches be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac you can run which

Anaconda distribution can be found here. Another advantage to installing Anaconda is that you don’t need admin rights to install it. Anaconda can install in the user’s home directory, which makes it trivial above. Handling ImportErrors If you encounter an ImportError, it usually means that Python couldn’t find pandas in the list of available libraries. Python internally has a list of directories it searches be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac you can run which

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1635 34.6.1.1 pandas.Index.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.6.1.2 pandas.Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1670 34.9.1.1 pandas.MultiIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1671 34.9.1.2 pandas.MultiIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1703 34.10.1.1 pandas.DatetimeIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1706 34.10.1.2 pandas.DatetimeIndex

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1728 34.6.1.1 pandas.Index.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1729 34.6.1.2 pandas.Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1769 34.10.1.1 pandas.MultiIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1770 34.10.1.2 pandas.MultiIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1805 34.11.1.1 pandas.DatetimeIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1808 34.11.1.2 pandas.DatetimeIndex

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1563 pandas.Index.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1564 pandas.Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1589 pandas.CategoricalIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1590 xxviii pandas.CategoricalIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1616 pandas.MultiIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1617 pandas.MultiIndex

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1566 pandas.Index.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1568 pandas.Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1593 xxviii pandas.CategoricalIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1594 pandas.CategoricalIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1620 pandas.MultiIndex.T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1622 pandas.MultiIndex

strings. 2. object dtype breaks dtype-specific operations like DataFrame.select_dtypes(). There isn’t a clear way to select just text while excluding non-text, but still object-dtype columns. 3. When reading PyPI (GH28341, GH20775). If you’re installing a built distribution (wheel) or via conda, this shouldn’t have any effect on you. If you’re building pandas from source, you should no longer need to install Categorical • Added test to assert the fillna() raises the correct ValueError message when the value isn’t a value from categories (GH13628) • Bug in Categorical.astype() where NaN values were handled incorrectly

Anaconda distribution can be found here. Another advantage to installing Anaconda is that you don’t need admin rights to install it. Anaconda can install in the user’s home directory, which makes it trivial above. Handling ImportErrors If you encounter an ImportError, it usually means that Python couldn’t find pandas in the list of available libraries. Python internally has a list of directories it searches be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac you can run which