Merge remote-tracking branch 'upstream/master'

Author: m-charlton

asv_bench/benchmarks/frame_methods.py

@@ -68,6 +68,8 @@ class Iteration(object):
     def setup(self):
         self.df = DataFrame(randn(10000, 1000))
         self.df2 = DataFrame(np.random.randn(50000, 10))
+        self.df3 = pd.DataFrame(np.random.randn(1000,5000),
+                                columns=['C'+str(c) for c in range(5000)])
 
     def f(self):
         if hasattr(self.df, '_item_cache'):
@@ -85,6 +87,11 @@ def time_iteritems(self):
     def time_iteritems_cached(self):
         self.g()
 
+    def time_iteritems_indexing(self):
+        df = self.df3
+        for col in df:
+            df[col]
+
     def time_itertuples(self):
         for row in self.df2.itertuples():
             pass

doc/cheatsheet/Pandas_Cheat_Sheet.pdf

@@ -0,0 +1,4 @@
+The Pandas Cheat Sheet was created using Microsoft Powerpoint 2013.
+To create the PDF version, within Powerpoint, simply do a "Save As"
+and pick "PDF' as the format.
+

doc/cheatsheet/Pandas_Cheat_Sheet.pptx

@@ -1757,17 +1757,27 @@ then the more *general* one will be used as the result of the operation.
    # conversion of dtypes
    df3.astype('float32').dtypes
 
-.. versionadded:: 0.20.0
 
 Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`
 
 .. ipython:: python
 
    dft = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
-   dft = dft.astype({'a': np.float64, 'c': np.uint8})
+   dft[['a','b']] = dft[['a','b']].astype(np.uint8)
    dft
    dft.dtypes
 
+.. versionadded:: 0.19.0
+
+Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFrame.astype`
+
+.. ipython:: python
+
+   dft1 = pd.DataFrame({'a': [1,0,1], 'b': [4,5,6], 'c': [7, 8, 9]})
+   dft1 = dft1.astype({'a': np.bool, 'c': np.float64})
+   dft1
+   dft1.dtypes
+
 .. note::
 
     When trying to convert a subset of columns to a specified type using :meth:`~DataFrame.astype`  and :meth:`~DataFrame.loc`, upcasting occurs.
@@ -1781,18 +1791,6 @@ Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`
        dft.loc[:, ['a', 'b']] = dft.loc[:, ['a', 'b']].astype(np.uint8)
        dft.dtypes
 
-
-.. versionadded:: 0.19
-
-Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFrame.astype`
-
-.. ipython:: python
-
-   dft1 = pd.DataFrame({'a': [1,0,1], 'b': [4,5,6], 'c': [7, 8, 9]})
-   dft1 = dft1.astype({'a': np.bool, 'c': np.float64})
-   dft1
-   dft1.dtypes
-
 .. _basics.object_conversion:
 
 object conversion