Merge branch 'master' into master · rs2/pandas@a1dbdf2 (original) (raw)
`@@ -2063,18 +2063,77 @@ def delitem(self, key):
`
2063
2063
``
2064
2064
`def take(self, indices, axis=0, convert=True, is_copy=True, **kwargs):
`
2065
2065
`"""
`
2066
``
`-
Analogous to ndarray.take
`
``
2066
`+
Return the elements in the given positional indices along an axis.
`
``
2067
+
``
2068
`+
This means that we are not indexing according to actual values in
`
``
2069
`+
the index attribute of the object. We are indexing according to the
`
``
2070
`+
actual position of the element in the object.
`
2067
2071
``
2068
2072
` Parameters
`
2069
2073
` ----------
`
2070
``
`-
indices : list / array of ints
`
``
2074
`+
indices : array-like
`
``
2075
`+
An array of ints indicating which positions to take.
`
2071
2076
` axis : int, default 0
`
2072
``
`-
convert : translate neg to pos indices (default)
`
2073
``
`-
is_copy : mark the returned frame as a copy
`
``
2077
`+
The axis on which to select elements. "0" means that we are
`
``
2078
`+
selecting rows, "1" means that we are selecting columns, etc.
`
``
2079
`+
convert : bool, default True
`
``
2080
`+
Whether to convert negative indices to positive ones, just as with
`
``
2081
`` +
indexing into Python lists. For example, if -1 was passed in,
``
``
2082
this index would be converted ``n - 1``.
``
2083
`+
is_copy : bool, default True
`
``
2084
`+
Whether to return a copy of the original object or not.
`
``
2085
+
``
2086
`+
Examples
`
``
2087
`+
`
``
2088
`+
df = pd.DataFrame([('falcon', 'bird', 389.0),
`
``
2089
`+
('parrot', 'bird', 24.0),
`
``
2090
`+
('lion', 'mammal', 80.5),
`
``
2091
`+
('monkey', 'mammal', np.nan)],
`
``
2092
`+
columns=('name', 'class', 'max_speed'),
`
``
2093
`+
index=[0, 2, 3, 1])
`
``
2094
`+
df
`
``
2095
`+
name class max_speed
`
``
2096
`+
0 falcon bird 389.0
`
``
2097
`+
2 parrot bird 24.0
`
``
2098
`+
3 lion mammal 80.5
`
``
2099
`+
1 monkey mammal NaN
`
``
2100
+
``
2101
`+
Take elements at positions 0 and 3 along the axis 0 (default).
`
``
2102
+
``
2103
`+
Note how the actual indices selected (0 and 1) do not correspond to
`
``
2104
`+
our selected indices 0 and 3. That's because we are selecting the 0th
`
``
2105
`+
and 3rd rows, not rows whose indices equal 0 and 3.
`
``
2106
+
``
2107
`+
df.take([0, 3])
`
``
2108
`+
0 falcon bird 389.0
`
``
2109
`+
1 monkey mammal NaN
`
``
2110
+
``
2111
`+
Take elements at indices 1 and 2 along the axis 1 (column selection).
`
``
2112
+
``
2113
`+
df.take([1, 2], axis=1)
`
``
2114
`+
class max_speed
`
``
2115
`+
0 bird 389.0
`
``
2116
`+
2 bird 24.0
`
``
2117
`+
3 mammal 80.5
`
``
2118
`+
1 mammal NaN
`
``
2119
+
``
2120
`+
We may take elements using negative integers for positive indices,
`
``
2121
`+
starting from the end of the object, just like with Python lists.
`
``
2122
+
``
2123
`+
df.take([-1, -2])
`
``
2124
`+
name class max_speed
`
``
2125
`+
1 monkey mammal NaN
`
``
2126
`+
3 lion mammal 80.5
`
2074
2127
``
2075
2128
` Returns
`
2076
2129
` -------
`
2077
2130
` taken : type of caller
`
``
2131
`+
An array-like containing the elements taken from the object.
`
``
2132
+
``
2133
`+
See Also
`
``
2134
`+
`
``
2135
`+
numpy.ndarray.take
`
``
2136
`+
numpy.take
`
2078
2137
` """
`
2079
2138
`nv.validate_take(tuple(), kwargs)
`
2080
2139
`self._consolidate_inplace()
`
`@@ -2978,14 +3037,36 @@ def filter(self, items=None, like=None, regex=None, axis=None):
`
2978
3037
``
2979
3038
`def head(self, n=5):
`
2980
3039
`"""
`
2981
``
`-
Returns first n rows
`
``
3040
`+
Return the first n rows.
`
``
3041
+
``
3042
`+
Parameters
`
``
3043
`+
`
``
3044
`+
n : int, default 5
`
``
3045
`+
Number of rows to select.
`
``
3046
+
``
3047
`+
Returns
`
``
3048
`+
`
``
3049
`+
obj_head : type of caller
`
``
3050
`+
The first n rows of the caller object.
`
2982
3051
` """
`
``
3052
+
2983
3053
`return self.iloc[:n]
`
2984
3054
``
2985
3055
`def tail(self, n=5):
`
2986
3056
`"""
`
2987
``
`-
Returns last n rows
`
``
3057
`+
Return the last n rows.
`
``
3058
+
``
3059
`+
Parameters
`
``
3060
`+
`
``
3061
`+
n : int, default 5
`
``
3062
`+
Number of rows to select.
`
``
3063
+
``
3064
`+
Returns
`
``
3065
`+
`
``
3066
`+
obj_tail : type of caller
`
``
3067
`+
The last n rows of the caller object.
`
2988
3068
` """
`
``
3069
+
2989
3070
`if n == 0:
`
2990
3071
`return self.iloc[0:0]
`
2991
3072
`return self.iloc[-n:]
`