code stringlengths 3 6.57k |
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np.abs(self) |
None (default) |
df.describe(include=['O']) |
None (default) |
None (default) |
df.describe(include=['O']) |
None (default) |
data (e.g. strings or timestamps) |
pd.Series([1, 2, 3]) |
s.describe() |
pd.Series(['a', 'a', 'b', 'c']) |
s.describe() |
np.datetime64("2000-01-01") |
np.datetime64("2010-01-01") |
np.datetime64("2010-01-01") |
s.describe() |
pd.DataFrame({'categorical': pd.Categorical(['d','e','f']) |
df.describe() |
df.describe(include='all') |
df.numeric.describe() |
df.describe(include=[np.number]) |
df.describe(include=[np.object]) |
df.describe(include=['category']) |
df.describe(exclude=[np.number]) |
df.describe(exclude=[np.object]) |
ValueError("Cannot describe a DataFrame without columns") |
list(percentiles) |
validate_percentile(percentiles) |
percentiles.append(0.5) |
np.asarray(percentiles) |
np.array([0.25, 0.5, 0.75]) |
np.unique(percentiles) |
len(unique_pcts) |
len(percentiles) |
ValueError("percentiles cannot contain duplicates") |
format_percentiles(percentiles) |
describe_numeric_1d(series) |
series.count() |
series.mean() |
series.std() |
series.min() |
series.quantile(percentiles) |
tolist() |
series.max() |
pd.Series(d, index=stat_index, name=series.name) |
describe_categorical_1d(data) |
data.value_counts() |
len(objcounts[objcounts != 0]) |
data.count() |
pd.Series(result, index=names, name=data.name, dtype=dtype) |
describe_timestamp_1d(data) |
data.count() |
data.mean() |
data.min() |
data.quantile(percentiles) |
tolist() |
data.max() |
pd.Series(d, index=stat_index, name=data.name) |
describe_1d(data) |
is_bool_dtype(data) |
describe_categorical_1d(data) |
is_numeric_dtype(data) |
describe_numeric_1d(data) |
is_datetime64_any_dtype(data) |
describe_timestamp_1d(data) |
is_timedelta64_dtype(data) |
describe_numeric_1d(data) |
describe_categorical_1d(data) |
describe_1d(self) |
elif (include is None) |
and (exclude is None) |
self.select_dtypes(include=[np.number]) |
len(data.columns) |
ValueError(msg) |
self.select_dtypes(include=include, exclude=exclude) |
describe_1d(s) |
data.items() |
sorted((x.index for x in ldesc) |
names.append(name) |
pd.concat([x.reindex(names, copy=False) |
data.columns.copy() |
API (e.g. 'M' or BDay() |
pd.Series([90, 91, 85]) |
s.pct_change() |
s.pct_change(periods=2) |
pd.Series([90, 91, None, 85]) |
s.pct_change(fill_method='ffill') |
df.pct_change() |
df.pct_change(axis='columns') |
Appender(_shared_docs["pct_change"] % _shared_doc_kwargs) |
self._get_axis_number(kwargs.pop("axis", self._stat_axis_name) |
self.fillna(method=fill_method, axis=axis, limit=limit) |
data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs) |
rs.index.duplicated() |
rs.reindex_like(data) |
_agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs) |
ValueError("Must specify 'axis' when aggregating by level.") |
self.groupby(level=level, axis=axis, sort=False) |
hasattr(grouped, name) |
getattr(grouped, name) |
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