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[Issue: #16416] Adding examples for some DataFrame methods #16437

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Merged
merged 7 commits into from
May 25, 2017
Merged

[Issue: #16416] Adding examples for some DataFrame methods #16437

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8877683
Adding examples to fillna
VincentLa May 22, 2017
88b1e76
putting better spacing
VincentLa May 22, 2017
ed78262
removing unnecessary values variable
VincentLa May 22, 2017
cee399e
Adding an example for method ffill
VincentLa May 22, 2017
4d0ce5d
adding examples for drop method
VincentLa May 22, 2017
9488585
Adding examples for sort_values
VincentLa May 23, 2017
fe13093
Fixing linter error
VincentLa May 23, 2017
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143 changes: 143 additions & 0 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2055,6 +2055,37 @@ def drop(self, labels, axis=0, level=None, inplace=False, errors='raise'):
Returns
-------
dropped : type of caller

Examples
--------
>>> df = pd.DataFrame([[1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 1, 2, 3],
... [4, 5, 6, 7]
... ],
... columns=list('ABCD'))
>>> df
A B C D
0 1 2 3 4
1 5 6 7 8
2 9 1 2 3
3 4 5 6 7

Drop a row by index

>>> df.drop([0, 1])
A B C D
2 9 1 2 3
3 4 5 6 7

Drop columns

>>> df.drop(['A', 'B'], axis=1)
C D
0 3 4
1 7 8
2 2 3
3 6 7
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
axis = self._get_axis_number(axis)
Expand Down Expand Up @@ -2168,6 +2199,66 @@ def add_suffix(self, suffix):
Returns
-------
sorted_obj : %(klass)s

Examples
--------
>>> df = pd.DataFrame({
... 'col1' : ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2' : [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3

Sort by col1

>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
"""

def sort_values(self, by, axis=0, ascending=True, inplace=False,
Expand Down Expand Up @@ -3468,6 +3559,58 @@ def convert_objects(self, convert_dates=True, convert_numeric=False,
Returns
-------
filled : %(klass)s

Examples
--------
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, 5],
... [np.nan, 3, np.nan, 4]],
... columns=list('ABCD'))
>>> df
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN 4

Replace all NaN elements with 0s.

>>> df.fillna(0)
A B C D
0 0.0 2.0 0.0 0
1 3.0 4.0 0.0 1
2 0.0 0.0 0.0 5
3 0.0 3.0 0.0 4

We can also propagate non-null values forward or backward.

>>> df.fillna(method='ffill')
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 3.0 4.0 NaN 5
3 3.0 3.0 NaN 4

Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
2, and 3 respectively.

>>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
>>> df.fillna(value=values)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 2.0 1
2 0.0 1.0 2.0 5
3 0.0 3.0 2.0 4

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 NaN 1
2 NaN 1.0 NaN 5
3 NaN 3.0 NaN 4
""")

@Appender(_shared_docs['fillna'] % _shared_doc_kwargs)
Expand Down