Python pandas.core.frame.DataFrame.round() Examples
The following are 7
code examples of pandas.core.frame.DataFrame.round().
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Example #1
| Source File: series.py From recruit with Apache License 2.0 | 6 votes |
|
def round(self, decimals=0, *args, **kwargs):
"""
Round each value in a Series to the given number of decimals.
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Series object
See Also
--------
numpy.around
DataFrame.round
"""
nv.validate_round(args, kwargs)
result = com.values_from_object(self).round(decimals)
result = self._constructor(result, index=self.index).__finalize__(self)
return result
Example #2
| Source File: series.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def round(self, decimals=0, *args, **kwargs):
"""
Round each value in a Series to the given number of decimals.
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Series object
See Also
--------
numpy.around
DataFrame.round
"""
nv.validate_round(args, kwargs)
result = com.values_from_object(self).round(decimals)
result = self._constructor(result, index=self.index).__finalize__(self)
return result
Example #3
| Source File: series.py From vnpy_crypto with MIT License | 5 votes |
|
def round(self, decimals=0, *args, **kwargs):
"""
Round each value in a Series to the given number of decimals.
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Series object
See Also
--------
numpy.around
DataFrame.round
"""
nv.validate_round(args, kwargs)
result = com._values_from_object(self).round(decimals)
result = self._constructor(result, index=self.index).__finalize__(self)
return result
Example #4
| Source File: series.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def round(self, decimals=0, *args, **kwargs):
"""
Round each value in a Series to the given number of decimals.
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Series object
See Also
--------
numpy.around
DataFrame.round
"""
nv.validate_round(args, kwargs)
result = _values_from_object(self).round(decimals)
result = self._constructor(result, index=self.index).__finalize__(self)
return result
Example #5
| Source File: series.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def round(self, decimals=0, *args, **kwargs):
"""
Round each value in a Series to the given number of decimals.
Parameters
----------
decimals : int
Number of decimal places to round to (default: 0).
If decimals is negative, it specifies the number of
positions to the left of the decimal point.
Returns
-------
Series object
See Also
--------
numpy.around
DataFrame.round
"""
nv.validate_round(args, kwargs)
result = _values_from_object(self).round(decimals)
result = self._constructor(result, index=self.index).__finalize__(self)
return result
Example #6
| Source File: series.py From recruit with Apache License 2.0 | 4 votes |
|
def corr(self, other, method='pearson', min_periods=None):
"""
Compute correlation with `other` Series, excluding missing values.
Parameters
----------
other : Series
method : {'pearson', 'kendall', 'spearman'} or callable
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarray
and returning a float
.. versionadded:: 0.24.0
min_periods : int, optional
Minimum number of observations needed to have a valid result
Returns
-------
correlation : float
Examples
--------
>>> histogram_intersection = lambda a, b: np.minimum(a, b
... ).sum().round(decimals=1)
>>> s1 = pd.Series([.2, .0, .6, .2])
>>> s2 = pd.Series([.3, .6, .0, .1])
>>> s1.corr(s2, method=histogram_intersection)
0.3
"""
this, other = self.align(other, join='inner', copy=False)
if len(this) == 0:
return np.nan
if method in ['pearson', 'spearman', 'kendall'] or callable(method):
return nanops.nancorr(this.values, other.values, method=method,
min_periods=min_periods)
raise ValueError("method must be either 'pearson', "
"'spearman', or 'kendall', '{method}' "
"was supplied".format(method=method))
Example #7
| Source File: series.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def corr(self, other, method='pearson', min_periods=None):
"""
Compute correlation with `other` Series, excluding missing values.
Parameters
----------
other : Series
method : {'pearson', 'kendall', 'spearman'} or callable
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarray
and returning a float
.. versionadded:: 0.24.0
min_periods : int, optional
Minimum number of observations needed to have a valid result
Returns
-------
correlation : float
Examples
--------
>>> histogram_intersection = lambda a, b: np.minimum(a, b
... ).sum().round(decimals=1)
>>> s1 = pd.Series([.2, .0, .6, .2])
>>> s2 = pd.Series([.3, .6, .0, .1])
>>> s1.corr(s2, method=histogram_intersection)
0.3
"""
this, other = self.align(other, join='inner', copy=False)
if len(this) == 0:
return np.nan
if method in ['pearson', 'spearman', 'kendall'] or callable(method):
return nanops.nancorr(this.values, other.values, method=method,
min_periods=min_periods)
raise ValueError("method must be either 'pearson', "
"'spearman', or 'kendall', '{method}' "
"was supplied".format(method=method))
