Merge pull request #4972 from alefnula/iss4967

CLN: change print to print() in docs

Author: jreback

doc/source/basics.rst

@@ -860,7 +860,7 @@ Thus, for example:
 .. ipython::
 
    In [0]: for col in df:
-      ...:     print col
+      ...:     print(col)
       ...:
 
 iteritems
@@ -878,8 +878,8 @@ For example:
 .. ipython::
 
    In [0]: for item, frame in wp.iteritems():
-      ...:     print item
-      ...:     print frame
+      ...:     print(item)
+      ...:     print(frame)
       ...:
 
 
@@ -895,19 +895,19 @@ containing the data in each row:
 .. ipython::
 
    In [0]: for row_index, row in df2.iterrows():
-      ...:     print '%s\n%s' % (row_index, row)
+      ...:     print('%s\n%s' % (row_index, row))
       ...:
 
 For instance, a contrived way to transpose the dataframe would be:
 
 .. ipython:: python
 
    df2 = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
-   print df2
-   print df2.T
+   print(df2)
+   print(df2.T)
 
    df2_t = DataFrame(dict((idx,values) for idx, values in df2.iterrows()))
-   print df2_t
+   print(df2_t)
 
 .. note::
 
@@ -918,8 +918,8 @@ For instance, a contrived way to transpose the dataframe would be:
 
       df_iter = DataFrame([[1, 1.0]], columns=['x', 'y'])
       row = next(df_iter.iterrows())[1]
-      print row['x'].dtype
-      print df_iter['x'].dtype
+      print(row['x'].dtype)
+      print(df_iter['x'].dtype)
 
 itertuples
 ~~~~~~~~~~
@@ -932,7 +932,8 @@ For instance,
 
 .. ipython:: python
 
-   for r in df2.itertuples(): print r
+   for r in df2.itertuples():
+       print(r)
 
 .. _basics.string_methods:
 

doc/source/dsintro.rst

@@ -586,7 +586,7 @@ R package):
 .. ipython:: python
 
    baseball = read_csv('data/baseball.csv')
-   print baseball
+   print(baseball)
 
 .. ipython:: python
    :suppress:
@@ -599,7 +599,7 @@ DataFrame in tabular form, though it won't always fit the console width:
 
 .. ipython:: python
 
-   print baseball.iloc[-20:, :12].to_string()
+   print(baseball.iloc[-20:, :12].to_string())
 
 New since 0.10.0, wide DataFrames will now be printed across multiple rows by
 default:

doc/source/gotchas.rst

@@ -372,7 +372,7 @@ of the new set of columns rather than the original ones:
 
 .. ipython:: python
 
-   print open('tmp.csv').read()
+   print(open('tmp.csv').read())
 
    date_spec = {'nominal': [1, 2], 'actual': [1, 3]}
    df = read_csv('tmp.csv', header=None,

doc/source/groupby.rst

@@ -282,17 +282,17 @@ natural and functions similarly to ``itertools.groupby``:
    In [4]: grouped = df.groupby('A')
 
    In [5]: for name, group in grouped:
-      ...:        print name
-      ...:        print group
+      ...:        print(name)
+      ...:        print(group)
       ...:
 
 In the case of grouping by multiple keys, the group name will be a tuple:
 
 .. ipython::
 
    In [5]: for name, group in df.groupby(['A', 'B']):
-      ...:        print name
-      ...:        print group
+      ...:        print(name)
+      ...:        print(group)
       ...:
 
 It's standard Python-fu but remember you can unpack the tuple in the for loop

doc/source/indexing.rst

@@ -1149,7 +1149,7 @@ and stop are **inclusive** in the label-based case:
 .. ipython:: python
 
    date1, date2 = dates[[2, 4]]
-   print date1, date2
+   print(date1, date2)
    df.ix[date1:date2]
    df['A'].ix[date1:date2]
 
@@ -1211,10 +1211,10 @@ scalar values, though setting arbitrary vectors is not yet supported:
 
    df2 = df[:4]
    df2['foo'] = 'bar'
-   print df2
+   print(df2)
    df2.ix[2] = np.nan
-   print df2
-   print df2.dtypes
+   print(df2)
+   print(df2.dtypes)
 
 .. _indexing.view_versus_copy:
 
@@ -1639,13 +1639,13 @@ instance:
    midx = MultiIndex(levels=[['zero', 'one'], ['x','y']],
                      labels=[[1,1,0,0],[1,0,1,0]])
    df = DataFrame(randn(4,2), index=midx)
-   print df
+   print(df)
    df2 = df.mean(level=0)
-   print df2
-   print df2.reindex(df.index, level=0)
+   print(df2)
+   print(df2.reindex(df.index, level=0))
    df_aligned, df2_aligned = df.align(df2, level=0)
-   print df_aligned
-   print df2_aligned
+   print(df_aligned)
+   print(df2_aligned)
 
 
 The need for sortedness with :class:`~pandas.MultiIndex`