expandas.core package

Submodules

class expandas.core.accessor.AccessorMethods(df, module_name=None, attrs=None)

Bases: object

Accessor to related functionalities.

class expandas.core.frame.ModelFrame(data, target=None, *args, **kwargs)

Bases: pandas.core.frame.DataFrame

Data structure subclassing pandas.DataFrame to define a metadata to specify target (response variable) and data (explanatory variable / features).

Parameters:

data : same as pandas.DataFrame

target : str or array-like

Column name or values to be used as target

args : arguments passed to pandas.DataFrame

kwargs : keyword arguments passed to pandas.DataFrame

Attributes

T Transpose index and columns
at
axes
blocks Internal property, property synonym for as_blocks()
cluster Property to access sklearn.cluster.
covariance Property to access sklearn.covariance.
cross_decomposition Property to access sklearn.cross_decomposition
cross_validation Property to access sklearn.cross_validation.
crv Property to access sklearn.cross_validation.
data Return data (explanatory variable / features)
decision Return current estimator’s decision function
decomposition Property to access sklearn.decomposition
dtypes Return the dtypes in this object
dummy Property to access sklearn.dummy
empty True if NDFrame is entirely empty [no items]
ensemble Property to access sklearn.ensemble.
estimator Return most recently used estimator
feature_extraction Property to access sklearn.feature_extraction.
feature_selection Property to access sklearn.feature_selection.
ftypes Return the ftypes (indication of sparse/dense and dtype) in this object.
gaussian_process Property to access sklearn.gaussian_process.
grid_search Property to access sklearn.grid_search.
iat
iloc
isotonic Property to access sklearn.isotonic.
ix
kernel_approximation Property to access sklearn.kernel_approximation
lda Property to access sklearn.lda
learning_curve Property to access sklearn.learning_curve.
linear_model Property to access sklearn.linear_model.
lm Property to access sklearn.linear_model.
loc
log_proba Return current estimator’s log probabilities
manifold Property to access sklearn.manifold.
metrics Property to access sklearn.metrics.
mixture Property to access sklearn.mixture
multiclass Property to access sklearn.multiclass.
naive_bayes Property to access sklearn.naive_bayes
ndim Number of axes / array dimensions
neighbors Property to access sklearn.neighbors.
neural_network Property to access sklearn.neural_network
pipeline Property to access sklearn.pipeline.
pp Property to access sklearn.preprocessing.
predicted Return current estimator’s predicted results
preprocessing Property to access sklearn.preprocessing.
proba Return current estimator’s probabilities
qda Property to access sklearn.qda
random_projection Property to access sklearn.random_projection.
semi_supervised Property to access sklearn.semi_supervised.
shape
size number of elements in the NDFrame
svm Property to access sklearn.svm.
target Return target (response variable)
target_name Return target column name
tree Property to access sklearn.tree
values Numpy representation of NDFrame
is_copy  

Methods

abs() Return an object with absolute value taken.
add(other[, axis, level, fill_value]) Binary operator add with support to substitute a fill_value for missing data in
add_prefix(prefix) Concatenate prefix string with panel items names.
add_suffix(suffix) Concatenate suffix string with panel items names
align(other[, join, axis, level, copy, ...]) Align two object on their axes with the
all([axis, bool_only, skipna, level]) Return whether all elements are True over requested axis
any([axis, bool_only, skipna, level]) Return whether any element is True over requested axis
append(other[, ignore_index, verify_integrity]) Append columns of other to end of this frame’s columns and index, returning a new object.
apply(func[, axis, broadcast, raw, reduce, args]) Applies function along input axis of DataFrame.
applymap(func) Apply a function to a DataFrame that is intended to operate elementwise, i.e.
as_blocks() Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.
as_matrix([columns]) Convert the frame to its Numpy-array representation.
asfreq(freq[, method, how, normalize]) Convert all TimeSeries inside to specified frequency using DateOffset objects.
astype(dtype[, copy, raise_on_error]) Cast object to input numpy.dtype
at_time(time[, asof]) Select values at particular time of day (e.g.
between_time(start_time, end_time[, ...]) Select values between particular times of the day (e.g., 9:00-9:30 AM)
bfill([axis, inplace, limit, downcast]) Synonym for NDFrame.fillna(method=’bfill’)
bool() Return the bool of a single element PandasObject
boxplot([column, by, ax, fontsize, rot, ...]) Make a box plot from DataFrame column optionally grouped by some columns or
clip([lower, upper, out]) Trim values at input threshold(s)
clip_lower(threshold) Return copy of the input with values below given value truncated
clip_upper(threshold) Return copy of input with values above given value truncated
combine(other, func[, fill_value, overwrite]) Add two DataFrame objects and do not propagate NaN values, so if for a
combineAdd(other) Add two DataFrame objects and do not propagate
combineMult(other) Multiply two DataFrame objects and do not propagate NaN values, so if
combine_first(other) Combine two DataFrame objects and default to non-null values in frame calling the method.
compound([axis, skipna, level]) Return the compound percentage of the values for the requested axis
consolidate([inplace]) Compute NDFrame with “consolidated” internals (data of each dtype grouped together in a single ndarray).
convert_objects([convert_dates, ...]) Attempt to infer better dtype for object columns
copy([deep]) Make a copy of this object
corr([method, min_periods]) Compute pairwise correlation of columns, excluding NA/null values
corrwith(other[, axis, drop]) Compute pairwise correlation between rows or columns of two DataFrame objects.
count([axis, level, numeric_only]) Return Series with number of non-NA/null observations over requested axis.
cov([min_periods]) Compute pairwise covariance of columns, excluding NA/null values
cummax([axis, dtype, out, skipna]) Return cumulative max over requested axis.
cummin([axis, dtype, out, skipna]) Return cumulative min over requested axis.
cumprod([axis, dtype, out, skipna]) Return cumulative prod over requested axis.
cumsum([axis, dtype, out, skipna]) Return cumulative sum over requested axis.
decision_function(estimator, *args, **kwargs) Call estimator’s decision_function method.
describe([percentile_width, percentiles, ...]) Generate various summary statistics, excluding NaN values.
diff([periods]) 1st discrete difference of object
div(other[, axis, level, fill_value]) Binary operator truediv with support to substitute a fill_value for missing data in
divide(other[, axis, level, fill_value]) Binary operator truediv with support to substitute a fill_value for missing data in
dot(other) Matrix multiplication with DataFrame or Series objects
drop(labels[, axis, level, inplace]) Return new object with labels in requested axis removed
drop_duplicates(*args, **kwargs) Return DataFrame with duplicate rows removed, optionally only
dropna([axis, how, thresh, subset, inplace]) Return object with labels on given axis omitted where alternately any
duplicated(*args, **kwargs) Return boolean Series denoting duplicate rows, optionally only
eq(other[, axis, level]) Wrapper for flexible comparison methods eq
equals(other) Determines if two NDFrame objects contain the same elements.
eval(expr, **kwargs) Evaluate an expression in the context of the calling DataFrame instance.
ffill([axis, inplace, limit, downcast]) Synonym for NDFrame.fillna(method=’ffill’)
fillna([value, method, axis, inplace, ...]) Fill NA/NaN values using the specified method
filter([items, like, regex, axis]) Restrict the info axis to set of items or wildcard
first(offset) Convenience method for subsetting initial periods of time series data
first_valid_index() Return label for first non-NA/null value
fit(estimator, *args, **kwargs) Call estimator’s fit method.
fit_predict(estimator, *args, **kwargs) Call estimator’s fit_predict method.
fit_transform(estimator, *args, **kwargs) Call estimator’s fit_transform method.
floordiv(other[, axis, level, fill_value]) Binary operator floordiv with support to substitute a fill_value for missing data in
from_csv(path[, header, sep, index_col, ...]) Read delimited file into DataFrame
from_dict(data[, orient, dtype]) Construct DataFrame from dict of array-like or dicts
from_items(items[, columns, orient]) Convert (key, value) pairs to DataFrame.
from_records(data[, index, exclude, ...]) Convert structured or record ndarray to DataFrame
ge(other[, axis, level]) Wrapper for flexible comparison methods ge
get(key[, default]) Get item from object for given key (DataFrame column, Panel slice, etc.).
get_dtype_counts() Return the counts of dtypes in this object
get_ftype_counts() Return the counts of ftypes in this object
get_value(index, col[, takeable]) Quickly retrieve single value at passed column and index
get_values() same as values (but handles sparseness conversions)
groupby([by, axis, level, as_index, sort, ...]) Group series using mapper (dict or key function, apply given function
gt(other[, axis, level]) Wrapper for flexible comparison methods gt
has_data() Return whether ModelFrame has data
has_target() Return whether ModelFrame has target
head([n]) Returns first n rows
hist(data[, column, by, grid, xlabelsize, ...]) Draw histogram of the DataFrame’s series using matplotlib / pylab.
icol(i)
idxmax([axis, skipna]) Return index of first occurrence of maximum over requested axis.
idxmin([axis, skipna]) Return index of first occurrence of minimum over requested axis.
iget_value(i, j)
info([verbose, buf, max_cols, memory_usage, ...]) Concise summary of a DataFrame.
insert(loc, column, value[, allow_duplicates]) Insert column into DataFrame at specified location.
interpolate([method, axis, limit, inplace, ...]) Interpolate values according to different methods.
inverse_transform(estimator, *args, **kwargs) Call estimator’s inverse_transform method.
irow(i[, copy])
isin(values) Return boolean DataFrame showing whether each element in the DataFrame is contained in values.
isnull() Return a boolean same-sized object indicating if the values are null
iteritems() Iterator over (column, series) pairs
iterkv(*args, **kwargs) iteritems alias used to get around 2to3. Deprecated
iterrows() Iterate over rows of DataFrame as (index, Series) pairs.
itertuples([index]) Iterate over rows of DataFrame as tuples, with index value
join(other[, on, how, lsuffix, rsuffix, sort]) Join columns with other DataFrame either on index or on a key column.
keys() Get the ‘info axis’ (see Indexing for more)
kurt([axis, skipna, level, numeric_only]) Return unbiased kurtosis over requested axis
kurtosis([axis, skipna, level, numeric_only]) Return unbiased kurtosis over requested axis
last(offset) Convenience method for subsetting final periods of time series data
last_valid_index() Return label for last non-NA/null value
le(other[, axis, level]) Wrapper for flexible comparison methods le
load(path) Deprecated.
lookup(row_labels, col_labels) Label-based “fancy indexing” function for DataFrame.
lt(other[, axis, level]) Wrapper for flexible comparison methods lt
mad([axis, skipna, level]) Return the mean absolute deviation of the values for the requested axis
mask(cond) Returns copy whose values are replaced with nan if the
max([axis, skipna, level, numeric_only]) This method returns the maximum of the values in the object.
mean([axis, skipna, level, numeric_only]) Return the mean of the values for the requested axis
median([axis, skipna, level, numeric_only]) Return the median of the values for the requested axis
memory_usage([index]) Memory usage of DataFrame columns.
merge(right[, how, on, left_on, right_on, ...]) Merge DataFrame objects by performing a database-style join operation by columns or indexes.
min([axis, skipna, level, numeric_only]) This method returns the minimum of the values in the object.
mod(other[, axis, level, fill_value]) Binary operator mod with support to substitute a fill_value for missing data in
mode([axis, numeric_only]) Gets the mode of each element along the axis selected.
mul(other[, axis, level, fill_value]) Binary operator mul with support to substitute a fill_value for missing data in
multiply(other[, axis, level, fill_value]) Binary operator mul with support to substitute a fill_value for missing data in
ne(other[, axis, level]) Wrapper for flexible comparison methods ne
notnull() Return a boolean same-sized object indicating if the values are
pct_change([periods, fill_method, limit, freq]) Percent change over given number of periods.
pivot([index, columns, values]) Reshape data (produce a “pivot” table) based on column values.
pivot_table(*args, **kwargs) Create a spreadsheet-style pivot table as a DataFrame.
plot(data[, x, y, kind, ax, subplots, ...]) Make plots of DataFrame using matplotlib / pylab.
pop(item) Return item and drop from frame.
pow(other[, axis, level, fill_value]) Binary operator pow with support to substitute a fill_value for missing data in
predict(estimator, *args, **kwargs) Call estimator’s predict method.
predict_log_proba(estimator, *args, **kwargs) Call estimator’s predict_log_proba method.
predict_proba(estimator, *args, **kwargs) Call estimator’s predict_proba method.
prod([axis, skipna, level, numeric_only]) Return the product of the values for the requested axis
product([axis, skipna, level, numeric_only]) Return the product of the values for the requested axis
quantile([q, axis, numeric_only]) Return values at the given quantile over requested axis, a la numpy.percentile.
query(expr, **kwargs) Query the columns of a frame with a boolean expression.
radd(other[, axis, level, fill_value]) Binary operator radd with support to substitute a fill_value for missing data in
rank([axis, numeric_only, method, ...]) Compute numerical data ranks (1 through n) along axis.
rdiv(other[, axis, level, fill_value]) Binary operator rtruediv with support to substitute a fill_value for missing data in
reindex([index, columns]) Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
reindex_axis(labels[, axis, method, level, ...]) Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
reindex_like(other[, method, copy, limit]) return an object with matching indicies to myself
rename([index, columns]) Alter axes input function or functions.
rename_axis(mapper[, axis, copy, inplace]) Alter index and / or columns using input function or functions.
reorder_levels(order[, axis]) Rearrange index levels using input order.
replace([to_replace, value, inplace, limit, ...]) Replace values given in ‘to_replace’ with ‘value’.
resample(rule[, how, axis, fill_method, ...]) Convenience method for frequency conversion and resampling of regular time-series data.
reset_index([level, drop, inplace, ...]) For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ‘level_0’, ‘level_1’, etc.
rfloordiv(other[, axis, level, fill_value]) Binary operator rfloordiv with support to substitute a fill_value for missing data in
rmod(other[, axis, level, fill_value]) Binary operator rmod with support to substitute a fill_value for missing data in
rmul(other[, axis, level, fill_value]) Binary operator rmul with support to substitute a fill_value for missing data in
rpow(other[, axis, level, fill_value]) Binary operator rpow with support to substitute a fill_value for missing data in
rsub(other[, axis, level, fill_value]) Binary operator rsub with support to substitute a fill_value for missing data in
rtruediv(other[, axis, level, fill_value]) Binary operator rtruediv with support to substitute a fill_value for missing data in
save(path) Deprecated.
score(estimator, *args, **kwargs) Call estimator’s score method.
select(crit[, axis]) Return data corresponding to axis labels matching criteria
select_dtypes([include, exclude]) Return a subset of a DataFrame including/excluding columns based on their dtype.
sem([axis, skipna, level, ddof]) Return unbiased standard error of the mean over requested axis.
set_axis(axis, labels) public verson of axis assignment
set_index(keys[, drop, append, inplace, ...]) Set the DataFrame index (row labels) using one or more existing columns.
set_value(index, col, value[, takeable]) Put single value at passed column and index
shift([periods, freq, axis]) Shift index by desired number of periods with an optional time freq
skew([axis, skipna, level, numeric_only]) Return unbiased skew over requested axis
slice_shift([periods, axis]) Equivalent to shift without copying data.
sort([columns, axis, ascending, inplace, ...]) Sort DataFrame either by labels (along either axis) or by the values in
sort_index([axis, by, ascending, inplace, ...]) Sort DataFrame either by labels (along either axis) or by the values in
sortlevel([level, axis, ascending, inplace, ...]) Sort multilevel index by chosen axis and primary level.
squeeze() squeeze length 1 dimensions
stack([level, dropna]) Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels.
std([axis, skipna, level, ddof]) Return unbiased standard deviation over requested axis.
sub(other[, axis, level, fill_value]) Binary operator sub with support to substitute a fill_value for missing data in
subtract(other[, axis, level, fill_value]) Binary operator sub with support to substitute a fill_value for missing data in
sum([axis, skipna, level, numeric_only]) Return the sum of the values for the requested axis
swapaxes(axis1, axis2[, copy]) Interchange axes and swap values axes appropriately
swaplevel(i, j[, axis]) Swap levels i and j in a MultiIndex on a particular axis
tail([n]) Returns last n rows
take(indices[, axis, convert, is_copy]) Analogous to ndarray.take
to_clipboard([excel, sep]) Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.
to_csv(*args, **kwargs) Write DataFrame to a comma-separated values (csv) file
to_dense() Return dense representation of NDFrame (as opposed to sparse)
to_dict(*args, **kwargs) Convert DataFrame to dictionary.
to_excel(*args, **kwargs) Write DataFrame to a excel sheet
to_gbq(destination_table[, project_id, ...]) Write a DataFrame to a Google BigQuery table.
to_hdf(path_or_buf, key, **kwargs) activate the HDFStore
to_html([buf, columns, col_space, colSpace, ...]) Render a DataFrame as an HTML table.
to_json([path_or_buf, orient, date_format, ...]) Convert the object to a JSON string.
to_latex([buf, columns, col_space, ...]) Render a DataFrame to a tabular environment table.
to_msgpack([path_or_buf]) msgpack (serialize) object to input file path
to_panel() Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
to_period([freq, axis, copy]) Convert DataFrame from DatetimeIndex to PeriodIndex with desired
to_pickle(path) Pickle (serialize) object to input file path
to_records([index, convert_datetime64]) Convert DataFrame to record array.
to_sparse([fill_value, kind]) Convert to SparseDataFrame
to_sql(name, con[, flavor, schema, ...]) Write records stored in a DataFrame to a SQL database.
to_stata(fname[, convert_dates, ...]) A class for writing Stata binary dta files from array-like objects
to_string([buf, columns, col_space, ...]) Render a DataFrame to a console-friendly tabular output.
to_timestamp([freq, how, axis, copy]) Cast to DatetimeIndex of timestamps, at beginning of period
to_wide(*args, **kwargs)
transform(estimator, *args, **kwargs) Call estimator’s transform method.
transpose() Transpose index and columns
truediv(other[, axis, level, fill_value]) Binary operator truediv with support to substitute a fill_value for missing data in
truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular dates.
tshift([periods, freq, axis]) Shift the time index, using the index’s frequency if available
tz_convert(tz[, axis, level, copy]) Convert the axis to target time zone.
tz_localize(*args, **kwargs) Localize tz-naive TimeSeries to target time zone
unstack([level]) Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.
update(other[, join, overwrite, ...]) Modify DataFrame in place using non-NA values from passed DataFrame.
var([axis, skipna, level, ddof]) Return unbiased variance over requested axis.
where(cond[, other, inplace, axis, level, ...]) Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
xs(key[, axis, level, copy, drop_level]) Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
cluster

Property to access sklearn.cluster. See expandas.skaccessors.cluster

covariance

Property to access sklearn.covariance. See expandas.skaccessors.covariance

cross_decomposition

Property to access sklearn.cross_decomposition

cross_validation

Property to access sklearn.cross_validation. See expandas.skaccessors.cross_validation

crv

Property to access sklearn.cross_validation. See expandas.skaccessors.cross_validation

data

Return data (explanatory variable / features)

Returns:data : ModelFrame
decision

Return current estimator’s decision function

Returns:decisions : ModelFrame
decision_function(estimator, *args, **kwargs)

Call estimator’s decision_function method.

Parameters:

args : arguments passed to decision_function method

kwargs : keyword arguments passed to decision_function method
Returns:

returned : decisions
decomposition

Property to access sklearn.decomposition

dummy

Property to access sklearn.dummy

ensemble

Property to access sklearn.ensemble. See expandas.skaccessors.ensemble

estimator

Return most recently used estimator

Returns:estimator : estimator
feature_extraction

Property to access sklearn.feature_extraction. See expandas.skaccessors.feature_extraction

feature_selection

Property to access sklearn.feature_selection. See expandas.skaccessors.feature_selection

fit(estimator, *args, **kwargs)

Call estimator’s fit method.

Parameters:

args : arguments passed to fit method

kwargs : keyword arguments passed to fit method
Returns:

returned : None or fitted estimator
fit_predict(estimator, *args, **kwargs)

Call estimator’s fit_predict method.

Parameters:

args : arguments passed to fit_predict method

kwargs : keyword arguments passed to fit_predict method
Returns:

returned : predicted result
fit_transform(estimator, *args, **kwargs)

Call estimator’s fit_transform method.

Parameters:

args : arguments passed to fit_transform method

kwargs : keyword arguments passed to fit_transform method
Returns:

returned : transformed result
gaussian_process

Property to access sklearn.gaussian_process. See expandas.skaccessors.gaussian_process

Property to access sklearn.grid_search. See expandas.skaccessors.grid_search

has_data()

Return whether ModelFrame has data

Returns:has_data : bool
has_target()

Return whether ModelFrame has target

Returns:has_target : bool
inverse_transform(estimator, *args, **kwargs)

Call estimator’s inverse_transform method.

Parameters:

args : arguments passed to inverse_transform method

kwargs : keyword arguments passed to inverse_transform method
Returns:

returned : transformed result
isotonic

Property to access sklearn.isotonic. See expandas.skaccessors.isotonic

kernel_approximation

Property to access sklearn.kernel_approximation

lda

Property to access sklearn.lda

learning_curve

Property to access sklearn.learning_curve. See expandas.skaccessors.learning_curve

linear_model

Property to access sklearn.linear_model. See expandas.skaccessors.linear_model

lm

Property to access sklearn.linear_model. See expandas.skaccessors.linear_model

log_proba

Return current estimator’s log probabilities

Returns:probabilities : ModelFrame
manifold

Property to access sklearn.manifold. See expandas.skaccessors.manifold

metrics

Property to access sklearn.metrics. See expandas.skaccessors.metrics

mixture

Property to access sklearn.mixture

multiclass

Property to access sklearn.multiclass. See expandas.skaccessors.multiclass

naive_bayes

Property to access sklearn.naive_bayes

neighbors

Property to access sklearn.neighbors. See expandas.skaccessors.neighbors

neural_network

Property to access sklearn.neural_network

pipeline

Property to access sklearn.pipeline. See expandas.skaccessors.pipeline

pp

Property to access sklearn.preprocessing. See expandas.skaccessors.preprocessing

predict(estimator, *args, **kwargs)

Call estimator’s predict method.

Parameters:

args : arguments passed to predict method

kwargs : keyword arguments passed to predict method
Returns:

returned : predicted result
predict_log_proba(estimator, *args, **kwargs)

Call estimator’s predict_log_proba method.

Parameters:

args : arguments passed to predict_log_proba method

kwargs : keyword arguments passed to predict_log_proba method
Returns:

returned : probabilities
predict_proba(estimator, *args, **kwargs)

Call estimator’s predict_proba method.

Parameters:

args : arguments passed to predict_proba method

kwargs : keyword arguments passed to predict_proba method
Returns:

returned : probabilities
predicted

Return current estimator’s predicted results

Returns:predicted : ModelSeries
preprocessing

Property to access sklearn.preprocessing. See expandas.skaccessors.preprocessing

proba

Return current estimator’s probabilities

Returns:probabilities : ModelFrame
qda

Property to access sklearn.qda

random_projection

Property to access sklearn.random_projection. See expandas.skaccessors.random_projection

score(estimator, *args, **kwargs)

Call estimator’s score method.

Parameters:

args : arguments passed to score method

kwargs : keyword arguments passed to score method
Returns:

returned : score
semi_supervised

Property to access sklearn.semi_supervised. See expandas.skaccessors.semi_supervised

svm

Property to access sklearn.svm. See expandas.skaccessors.svm

target

Return target (response variable)

Returns:target : ModelSeries
target_name

Return target column name

Returns:target : object
transform(estimator, *args, **kwargs)

Call estimator’s transform method.

Parameters:

args : arguments passed to transform method

kwargs : keyword arguments passed to transform method
Returns:

returned : transformed result
tree

Property to access sklearn.tree

class expandas.core.series.ModelSeries(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

Bases: pandas.core.series.Series

Wrapper for pandas.Series to support sklearn.preprocessing

Attributes

T return the transpose, which is by definition self
at
axes
base return the base object if the memory of the underlying data is shared
blocks Internal property, property synonym for as_blocks()
data return the data pointer of the underlying data
dtype return the dtype object of the underlying data
dtypes return the dtype object of the underlying data
empty True if NDFrame is entirely empty [no items]
flags return the ndarray.flags for the underlying data
ftype return if the data is sparse|dense
ftypes return if the data is sparse|dense
iat
iloc
imag
is_time_series
itemsize return the size of the dtype of the item of the underlying data
ix
loc
nbytes return the number of bytes in the underlying data
ndim return the number of dimensions of the underlying data, by definition 1
pp Property to access sklearn.preprocessing.
preprocessing Property to access sklearn.preprocessing.
real
shape return a tuple of the shape of the underlying data
size return the number of elements in the underlying data
strides return the strides of the underlying data
values Return Series as ndarray
cat  
dt  
is_copy  
str  

Methods

abs() Return an object with absolute value taken.
add(other[, level, fill_value, axis]) Binary operator add with support to substitute a fill_value for missing data
add_prefix(prefix) Concatenate prefix string with panel items names.
add_suffix(suffix) Concatenate suffix string with panel items names
align(other[, join, axis, level, copy, ...]) Align two object on their axes with the
all([axis, bool_only, skipna, level]) Return whether all elements are True over requested axis
any([axis, bool_only, skipna, level]) Return whether any element is True over requested axis
append(to_append[, verify_integrity]) Concatenate two or more Series.
apply(func[, convert_dtype, args]) Invoke function on values of Series.
argmax([axis, out, skipna]) Index of first occurrence of maximum of values.
argmin([axis, out, skipna]) Index of first occurrence of minimum of values.
argsort([axis, kind, order]) Overrides ndarray.argsort.
as_blocks() Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.
as_matrix([columns]) Convert the frame to its Numpy-array representation.
asfreq(freq[, method, how, normalize]) Convert all TimeSeries inside to specified frequency using DateOffset objects.
asof(where) Return last good (non-NaN) value in TimeSeries if value is NaN for requested date.
astype(dtype[, copy, raise_on_error]) Cast object to input numpy.dtype
at_time(time[, asof]) Select values at particular time of day (e.g.
autocorr() Lag-1 autocorrelation
between(left, right[, inclusive]) Return boolean Series equivalent to left <= series <= right.
between_time(start_time, end_time[, ...]) Select values between particular times of the day (e.g., 9:00-9:30 AM)
bfill([axis, inplace, limit, downcast]) Synonym for NDFrame.fillna(method=’bfill’)
bool() Return the bool of a single element PandasObject
clip([lower, upper, out]) Trim values at input threshold(s)
clip_lower(threshold) Return copy of the input with values below given value truncated
clip_upper(threshold) Return copy of input with values above given value truncated
combine(other, func[, fill_value]) Perform elementwise binary operation on two Series using given function
combine_first(other) Combine Series values, choosing the calling Series’s values first.
compound([axis, skipna, level]) Return the compound percentage of the values for the requested axis
compress(condition[, axis, out]) Return selected slices of an array along given axis as a Series
consolidate([inplace]) Compute NDFrame with “consolidated” internals (data of each dtype grouped together in a single ndarray).
convert_objects([convert_dates, ...]) Attempt to infer better dtype for object columns
copy([deep]) Make a copy of this object
corr(other[, method, min_periods]) Compute correlation with other Series, excluding missing values
count([level]) Return number of non-NA/null observations in the Series
cov(other[, min_periods]) Compute covariance with Series, excluding missing values
cummax([axis, dtype, out, skipna]) Return cumulative max over requested axis.
cummin([axis, dtype, out, skipna]) Return cumulative min over requested axis.
cumprod([axis, dtype, out, skipna]) Return cumulative prod over requested axis.
cumsum([axis, dtype, out, skipna]) Return cumulative sum over requested axis.
describe([percentile_width, percentiles, ...]) Generate various summary statistics, excluding NaN values.
diff([periods]) 1st discrete difference of object
div(other[, level, fill_value, axis]) Binary operator truediv with support to substitute a fill_value for missing data
divide(other[, level, fill_value, axis]) Binary operator truediv with support to substitute a fill_value for missing data
dot(other) Matrix multiplication with DataFrame or inner-product with Series
drop(labels[, axis, level, inplace]) Return new object with labels in requested axis removed
drop_duplicates([take_last, inplace]) Return Series with duplicate values removed
dropna([axis, inplace]) Return Series without null values
duplicated([take_last]) Return boolean Series denoting duplicate values
eq(other)
equals(other) Determines if two NDFrame objects contain the same elements.
factorize([sort, na_sentinel]) Encode the object as an enumerated type or categorical variable
ffill([axis, inplace, limit, downcast]) Synonym for NDFrame.fillna(method=’ffill’)
fillna([value, method, axis, inplace, ...]) Fill NA/NaN values using the specified method
filter([items, like, regex, axis]) Restrict the info axis to set of items or wildcard
first(offset) Convenience method for subsetting initial periods of time series data
first_valid_index() Return label for first non-NA/null value
floordiv(other[, level, fill_value, axis]) Binary operator floordiv with support to substitute a fill_value for missing data
from_array(arr[, index, name, dtype, copy, ...])
from_csv(path[, sep, parse_dates, header, ...]) Read delimited file into Series
ge(other)
get(key[, default]) Get item from object for given key (DataFrame column, Panel slice, etc.).
get_dtype_counts() Return the counts of dtypes in this object
get_ftype_counts() Return the counts of ftypes in this object
get_value(label[, takeable]) Quickly retrieve single value at passed index label
get_values() same as values (but handles sparseness conversions); is a view
groupby([by, axis, level, as_index, sort, ...]) Group series using mapper (dict or key function, apply given function
gt(other)
hasnans() return if I have any nans; enables various perf speedups
head([n]) Returns first n rows
hist([by, ax, grid, xlabelsize, xrot, ...]) Draw histogram of the input series using matplotlib
idxmax([axis, out, skipna]) Index of first occurrence of maximum of values.
idxmin([axis, out, skipna]) Index of first occurrence of minimum of values.
iget(i[, axis]) Return the i-th value or values in the Series by location
iget_value(i[, axis]) Return the i-th value or values in the Series by location
interpolate([method, axis, limit, inplace, ...]) Interpolate values according to different methods.
irow(i[, axis]) Return the i-th value or values in the Series by location
isin(values) Return a boolean Series showing whether each element in the Series is exactly contained in the passed sequence of values.
isnull() Return a boolean same-sized object indicating if the values are null
item() return the first element of the underlying data as a python scalar
iteritems() Lazily iterate over (index, value) tuples
iterkv(*args, **kwargs) iteritems alias used to get around 2to3. Deprecated
keys() Alias for index
kurt([axis, skipna, level, numeric_only]) Return unbiased kurtosis over requested axis
kurtosis([axis, skipna, level, numeric_only]) Return unbiased kurtosis over requested axis
last(offset) Convenience method for subsetting final periods of time series data
last_valid_index() Return label for last non-NA/null value
le(other)
load(path) Deprecated.
lt(other)
mad([axis, skipna, level]) Return the mean absolute deviation of the values for the requested axis
map(arg[, na_action]) Map values of Series using input correspondence (which can be
mask(cond) Returns copy whose values are replaced with nan if the
max([axis, skipna, level, numeric_only]) This method returns the maximum of the values in the object.
mean([axis, skipna, level, numeric_only]) Return the mean of the values for the requested axis
median([axis, skipna, level, numeric_only]) Return the median of the values for the requested axis
min([axis, skipna, level, numeric_only]) This method returns the minimum of the values in the object.
mod(other[, level, fill_value, axis]) Binary operator mod with support to substitute a fill_value for missing data
mode() Returns the mode(s) of the dataset.
mul(other[, level, fill_value, axis]) Binary operator mul with support to substitute a fill_value for missing data
multiply(other[, level, fill_value, axis]) Binary operator mul with support to substitute a fill_value for missing data
ne(other)
nlargest([n, take_last]) Return the largest n elements.
nonzero() Return the indices of the elements that are non-zero
notnull() Return a boolean same-sized object indicating if the values are
nsmallest([n, take_last]) Return the smallest n elements.
nunique([dropna]) Return number of unique elements in the object.
order([na_last, ascending, kind, ...]) Sorts Series object, by value, maintaining index-value link.
pct_change([periods, fill_method, limit, freq]) Percent change over given number of periods.
plot(data[, kind, ax, figsize, use_index, ...]) Make plots of Series using matplotlib / pylab.
pop(item) Return item and drop from frame.
pow(other[, level, fill_value, axis]) Binary operator pow with support to substitute a fill_value for missing data
prod([axis, skipna, level, numeric_only]) Return the product of the values for the requested axis
product([axis, skipna, level, numeric_only]) Return the product of the values for the requested axis
ptp([axis, out])
put(*args, **kwargs) return a ndarray with the values put
quantile([q]) Return value at the given quantile, a la numpy.percentile.
radd(other[, level, fill_value, axis]) Binary operator radd with support to substitute a fill_value for missing data
rank([method, na_option, ascending, pct]) Compute data ranks (1 through n).
ravel([order]) Return the flattened underlying data as an ndarray
rdiv(other[, level, fill_value, axis]) Binary operator rtruediv with support to substitute a fill_value for missing data
reindex([index]) Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
reindex_axis(labels[, axis]) for compatibility with higher dims
reindex_like(other[, method, copy, limit]) return an object with matching indicies to myself
rename([index]) Alter axes input function or functions.
rename_axis(mapper[, axis, copy, inplace]) Alter index and / or columns using input function or functions.
reorder_levels(order) Rearrange index levels using input order.
repeat(reps) return a new Series with the values repeated reps times
replace([to_replace, value, inplace, limit, ...]) Replace values given in ‘to_replace’ with ‘value’.
resample(rule[, how, axis, fill_method, ...]) Convenience method for frequency conversion and resampling of regular time-series data.
reset_index([level, drop, name, inplace]) Analogous to the pandas.DataFrame.reset_index() function, see docstring there.
reshape(*args, **kwargs) return an ndarray with the values shape
rfloordiv(other[, level, fill_value, axis]) Binary operator rfloordiv with support to substitute a fill_value for missing data
rmod(other[, level, fill_value, axis]) Binary operator rmod with support to substitute a fill_value for missing data
rmul(other[, level, fill_value, axis]) Binary operator rmul with support to substitute a fill_value for missing data
round([decimals, out]) Return a with each element rounded to the given number of decimals.
rpow(other[, level, fill_value, axis]) Binary operator rpow with support to substitute a fill_value for missing data
rsub(other[, level, fill_value, axis]) Binary operator rsub with support to substitute a fill_value for missing data
rtruediv(other[, level, fill_value, axis]) Binary operator rtruediv with support to substitute a fill_value for missing data
save(path) Deprecated.
searchsorted(v[, side, sorter]) Find indices where elements should be inserted to maintain order.
select(crit[, axis]) Return data corresponding to axis labels matching criteria
sem([axis, skipna, level, ddof]) Return unbiased standard error of the mean over requested axis.
set_axis(axis, labels) public verson of axis assignment
set_value(label, value[, takeable]) Quickly set single value at passed label.
shift([periods, freq, axis]) Shift index by desired number of periods with an optional time freq
skew([axis, skipna, level, numeric_only]) Return unbiased skew over requested axis
slice_shift([periods, axis]) Equivalent to shift without copying data.
sort([axis, ascending, kind, na_position, ...]) Sort values and index labels by value.
sort_index([ascending]) Sort object by labels (along an axis)
sortlevel([level, ascending, sort_remaining]) Sort Series with MultiIndex by chosen level.
squeeze() squeeze length 1 dimensions
std([axis, skipna, level, ddof]) Return unbiased standard deviation over requested axis.
sub(other[, level, fill_value, axis]) Binary operator sub with support to substitute a fill_value for missing data
subtract(other[, level, fill_value, axis]) Binary operator sub with support to substitute a fill_value for missing data
sum([axis, skipna, level, numeric_only]) Return the sum of the values for the requested axis
swapaxes(axis1, axis2[, copy]) Interchange axes and swap values axes appropriately
swaplevel(i, j[, copy]) Swap levels i and j in a MultiIndex
tail([n]) Returns last n rows
take(indices[, axis, convert, is_copy]) return Series corresponding to requested indices
to_clipboard([excel, sep]) Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.
to_csv(path[, index, sep, na_rep, ...]) Write Series to a comma-separated values (csv) file
to_dense() Return dense representation of NDFrame (as opposed to sparse)
to_dict() Convert Series to {label -> value} dict
to_frame([name]) Convert Series to DataFrame
to_hdf(path_or_buf, key, **kwargs) activate the HDFStore
to_json([path_or_buf, orient, date_format, ...]) Convert the object to a JSON string.
to_msgpack([path_or_buf]) msgpack (serialize) object to input file path
to_period([freq, copy]) Convert TimeSeries from DatetimeIndex to PeriodIndex with desired
to_pickle(path) Pickle (serialize) object to input file path
to_sparse([kind, fill_value]) Convert Series to SparseSeries
to_sql(name, con[, flavor, schema, ...]) Write records stored in a DataFrame to a SQL database.
to_string([buf, na_rep, float_format, ...]) Render a string representation of the Series
to_timestamp([freq, how, copy]) Cast to datetimeindex of timestamps, at beginning of period
tolist() Convert Series to a nested list
transpose() return the transpose, which is by definition self
truediv(other[, level, fill_value, axis]) Binary operator truediv with support to substitute a fill_value for missing data
truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular dates.
tshift([periods, freq, axis]) Shift the time index, using the index’s frequency if available
tz_convert(tz[, axis, level, copy]) Convert the axis to target time zone.
tz_localize(*args, **kwargs) Localize tz-naive TimeSeries to target time zone
unique() Return array of unique values in the object.
unstack([level]) Unstack, a.k.a.
update(other) Modify Series in place using non-NA values from passed Series.
valid([inplace])
value_counts([normalize, sort, ascending, ...]) Returns object containing counts of unique values.
var([axis, skipna, level, ddof]) Return unbiased variance over requested axis.
view([dtype])
where(cond[, other, inplace, axis, level, ...]) Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
xs(key[, axis, level, copy, drop_level]) Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
pp

Property to access sklearn.preprocessing. See expandas.skaccessors.preprocessing

preprocessing

Property to access sklearn.preprocessing. See expandas.skaccessors.preprocessing

to_frame(name=None)

Convert Series to DataFrame

Parameters:

name : object, default None

The passed name should substitute for the series name (if it has one).
Returns:

data_frame : DataFrame

Module contents