code stringlengths 3 6.57k |
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df.fillna(df.mode() |
DataFrame (sorted) |
pd.DataFrame({'A': [1, 2, 1, 2, 1, 2, 3]}) |
df.mode() |
self._get_numeric_data() |
s.mode() |
data.apply(f, axis=axis) |
quantile(self, q=0.5, axis=0, numeric_only=True) |
quantile(s) |
DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]) |
df.quantile(.1) |
df.quantile([.1, .5]) |
np.asarray(q) |
com.is_list_like(per) |
f(arr, per) |
_values_from_object(arr) |
view('i8') |
arr.astype(float) |
notnull(values) |
len(values) |
_quantile(values, per) |
self._get_numeric_data() |
data.dtypes.map(com.is_datetime64_dtype) |
f(vals, x) |
for (_, vals) |
data.iteritems() |
DataFrame(quantiles, index=data._info_axis, columns=q) |
len(is_dt_col) |
applymap(lib.Timestamp) |
result.T.squeeze() |
ranks (1 through n) |
columns (0) |
rows (1) |
high (1) |
low (N) |
self._get_axis_number(axis) |
self._get_numeric_data() |
self._constructor(ranks, index=data.index, columns=data.columns) |
to_timestamp(self, freq=None, how='start', axis=0, copy=True) |
convert (the index by default) |
new_data.copy() |
self._get_axis_number(axis) |
new_data.set_axis(1, self.index.to_timestamp(freq=freq, how=how) |
new_data.set_axis(0, self.columns.to_timestamp(freq=freq, how=how) |
AssertionError('Axis must be 0 or 1. Got %s' % str(axis) |
self._constructor(new_data) |
to_period(self, freq=None, axis=0, copy=True) |
frequency (inferred from index if not passed) |
convert (the index by default) |
new_data.copy() |
self._get_axis_number(axis) |
new_data.set_axis(1, self.index.to_period(freq=freq) |
new_data.set_axis(0, self.columns.to_period(freq=freq) |
AssertionError('Axis must be 0 or 1. Got %s' % str(axis) |
self._constructor(new_data) |
isin(self, values) |
DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']}) |
df.isin([1, 3, 12, 'a']) |
DataFrame({'A': [1, 2, 3], 'B': [1, 4, 7]}) |
df.isin({'A': [1, 3], 'B': [4, 7, 12]}) |
DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']}) |
DataFrame({'A': [1, 3, 3, 2], 'B': ['e', 'f', 'f', 'e']}) |
df.isin(other) |
isinstance(values, dict) |
defaultdict(list, values) |
concat((self.iloc[:, [i]].isin(values[col]) |
enumerate(self.columns) |
isinstance(values, Series) |
self.eq(values.reindex_like(self) |
isinstance(values, DataFrame) |
not (values.columns.is_unique and values.index.is_unique) |
self.eq(values.reindex_like(self) |
is_list_like(values) |
DataFrame.isin() |
format(type(values) |
DataFrame(lib.ismember(self.values.ravel() |
set(values) |
reshape(self.shape) |
combineAdd(self, other) |
a (column, time) |
self.add(other, fill_value=0.) |
combineMult(self, other) |
a (column, time) |
value (which might be NaN as well) |
self.mul(other, fill_value=1.) |
DataFrame._add_numeric_operations() |
Series([]) |
_arrays_to_mgr(arrays, arr_names, index, columns, dtype=None) |
extract_index(arrays) |
_ensure_index(index) |
_homogenize(arrays, index, dtype) |
_ensure_index(columns) |
_ensure_index(index) |
create_block_manager_from_arrays(arrays, arr_names, axes) |
extract_index(data) |
len(data) |
Index([]) |
len(data) |
isinstance(v, Series) |
indexes.append(v.index) |
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