abs() Return an object with absolute value taken–only applicable to objects that are all numeric. add(other[, level, fill_value, axis]) Addition of series and other, element-wise (binary operator add). add_prefix(prefix) Concatenate prefix string with panel items names. add_suffix(suffix) Concatenate suffix string with panel items names. agg(func[, axis]) Aggregate using callable, string, dict, or list of string/callables aggregate(func[, axis]) Aggregate using callable, string, dict, or list of string/callables 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[, ignore_index, ...]) Concatenate two or more Series. apply(func[, convert_dtype, args]) Invoke function on values of Series. argmax([axis, skipna]) Index of first occurrence of maximum of values. argmin([axis, skipna]) Index of first occurrence of minimum of values. argsort([axis, kind, order]) Overrides ndarray.argsort. as_blocks([copy]) 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 TimeSeries to specified frequency. asof(where[, subset]) The last row without any NaN is taken (or the last row without astype(dtype[, copy, errors]) Cast object to input numpy.dtype at_time(time[, asof]) Select values at particular time of day (e.g. autocorr([lag]) Lag-N 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 DataFrame.fillna(method='bfill') bool() Return the bool of a single element PandasObject. cat alias of CategoricalAccessor clip([lower, upper, axis]) Trim values at input threshold(s). clip_lower(threshold[, axis]) Return copy of the input with values below given value(s) truncated. clip_upper(threshold[, axis]) Return copy of input with values above given value(s) 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, *args, **kwargs) Return selected slices of an array along given axis as a Series consolidate([inplace]) DEPRECATED: consolidate will be an internal implementation only. convert_objects([convert_dates, ...]) Deprecated. copy([deep]) Make a copy of this objects data. 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, skipna]) Return cumulative max over requested axis. cummin([axis, skipna]) Return cumulative minimum over requested axis. cumprod([axis, skipna]) Return cumulative product over requested axis. cumsum([axis, skipna]) Return cumulative sum over requested axis. describe([percentiles, include, exclude]) Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. diff([periods]) 1st discrete difference of object div(other[, level, fill_value, axis]) Floating division of series and other, element-wise (binary operator truediv). divide(other[, level, fill_value, axis]) Floating division of series and other, element-wise (binary operator truediv). dot(other) Matrix multiplication with DataFrame or inner-product with Series drop(labels[, axis, level, inplace, errors]) Return new object with labels in requested axis removed. drop_duplicates([keep, inplace]) Return Series with duplicate values removed dropna([axis, inplace]) Return Series without null values dt alias of CombinedDatetimelikeProperties duplicated([keep]) Return boolean Series denoting duplicate values eq(other[, level, fill_value, axis]) Equal to of series and other, element-wise (binary operator eq). equals(other) Determines if two NDFrame objects contain the same elements. ewm([com, span, halflife, alpha, ...]) Provides exponential weighted functions expanding([min_periods, freq, center, axis]) Provides expanding transformations. factorize([sort, na_sentinel]) Encode the object as an enumerated type or categorical variable ffill([axis, inplace, limit, downcast]) Synonym for DataFrame.fillna(method='ffill') fillna([value, method, axis, inplace, ...]) Fill NA/NaN values using the specified method filter([items, like, regex, axis]) Subset rows or columns of dataframe according to labels in the specified index. first(offset) Convenience method for subsetting initial periods of time series data based on a date offset. first_valid_index() Return label for first non-NA/null value floordiv(other[, level, fill_value, axis]) Integer division of series and other, element-wise (binary operator floordiv). from_array(arr[, index, name, dtype, copy, ...]) from_csv(path[, sep, parse_dates, header, ...]) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). ge(other[, level, fill_value, axis]) Greater than or equal to of series and other, element-wise (binary operator 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(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 to group, return result as series) or by a series of columns. gt(other[, level, fill_value, axis]) Greater than of series and other, element-wise (binary operator gt). head([n]) Returns first n rows hist([by, ax, grid, xlabelsize, xrot, ...]) Draw histogram of the input series using matplotlib idxmax([axis, skipna]) Index of first occurrence of maximum of values. idxmin([axis, skipna]) Index of first occurrence of minimum of values. interpolate([method, axis, limit, inplace, ...]) Interpolate values according to different methods. 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 items() Lazily iterate over (index, value) tuples iteritems() Lazily iterate over (index, value) tuples keys() Alias for index kurt([axis, skipna, level, numeric_only]) Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). kurtosis([axis, skipna, level, numeric_only]) Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). last(offset) Convenience method for subsetting final periods of time series data based on a date offset. last_valid_index() Return label for last non-NA/null value le(other[, level, fill_value, axis]) Less than or equal to of series and other, element-wise (binary operator le). lt(other[, level, fill_value, axis]) Less than of series and other, element-wise (binary operator lt). 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[, other, inplace, axis, level, ...]) Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other. 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, deep]) Memory usage of the Series min([axis, skipna, level, numeric_only]) This method returns the minimum of the values in the object. mod(other[, level, fill_value, axis]) Modulo of series and other, element-wise (binary operator mod). mode() Return the mode(s) of the dataset. mul(other[, level, fill_value, axis]) Multiplication of series and other, element-wise (binary operator mul). multiply(other[, level, fill_value, axis]) Multiplication of series and other, element-wise (binary operator mul). ne(other[, level, fill_value, axis]) Not equal to of series and other, element-wise (binary operator ne). nlargest([n, keep]) 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 not null. nsmallest([n, keep]) Return the smallest n elements. nunique([dropna]) Return number of unique elements in the object. pct_change([periods, fill_method, limit, freq]) Percent change over given number of periods. pipe(func, *args, **kwargs) Apply func(self, *args, **kwargs) plot alias of SeriesPlotMethods pop(item) Return item and drop from frame. pow(other[, level, fill_value, axis]) Exponential power of series and other, element-wise (binary operator pow). 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, skipna, level, numeric_only]) Returns the difference between the maximum value and the minimum value in the object. put(*args, **kwargs) Applies the put method to its values attribute if it has one. quantile([q, interpolation]) Return value at the given quantile, a la numpy.percentile. radd(other[, level, fill_value, axis]) Addition of series and other, element-wise (binary operator radd). rank([axis, method, numeric_only, ...]) Compute numerical data ranks (1 through n) along axis. ravel([order]) Return the flattened underlying data as an ndarray rdiv(other[, level, fill_value, axis]) Floating division of series and other, element-wise (binary operator rtruediv). 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 indices 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(repeats, *args, **kwargs) Repeat elements of an Series. 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 time series. reset_index([level, drop, name, inplace]) Analogous to the pandas.DataFrame.reset_index() function, see docstring there. reshape(*args, **kwargs) DEPRECATED: calling this method will raise an error in a future release. rfloordiv(other[, level, fill_value, axis]) Integer division of series and other, element-wise (binary operator rfloordiv). rmod(other[, level, fill_value, axis]) Modulo of series and other, element-wise (binary operator rmod). rmul(other[, level, fill_value, axis]) Multiplication of series and other, element-wise (binary operator rmul). rolling(window[, min_periods, freq, center, ...]) Provides rolling window calculcations. round([decimals]) Round each value in a Series to the given number of decimals. rpow(other[, level, fill_value, axis]) Exponential power of series and other, element-wise (binary operator rpow). rsub(other[, level, fill_value, axis]) Subtraction of series and other, element-wise (binary operator rsub). rtruediv(other[, level, fill_value, axis]) Floating division of series and other, element-wise (binary operator rtruediv). sample([n, frac, replace, weights, ...]) Returns a random sample of items from an axis of object. searchsorted(value[, 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, numeric_only]) 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_index([axis, level, ascending, ...]) Sort object by labels (along an axis) sort_values([axis, ascending, inplace, ...]) Sort by the values along either axis sortlevel([level, ascending, sort_remaining]) DEPRECATED: use Series.sort_index() squeeze([axis]) Squeeze length 1 dimensions. std([axis, skipna, level, ddof, numeric_only]) Return sample standard deviation over requested axis. str alias of StringMethods sub(other[, level, fill_value, axis]) Subtraction of series and other, element-wise (binary operator sub). subtract(other[, level, fill_value, axis]) Subtraction of series and other, element-wise (binary operator sub). 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_excel(excel_writer[, sheet_name, na_rep, ...]) Write Series to an excel sheet to_frame([name]) Convert Series to DataFrame to_hdf(path_or_buf, key, **kwargs) Write the contained data to an HDF5 file using HDFStore. to_json([path_or_buf, orient, date_format, ...]) Convert the object to a JSON string. to_latex([buf, columns, col_space, header, ...]) Render an object to a tabular environment table. to_msgpack([path_or_buf, encoding]) msgpack (serialize) object to input file path to_period([freq, copy]) Convert Series from DatetimeIndex to PeriodIndex with desired to_pickle(path[, compression]) 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 to_xarray() Return an xarray object from the pandas object. tolist() Convert Series to a nested list transform(func, *args, **kwargs) Call function producing a like-indexed NDFrame transpose(*args, **kwargs) return the transpose, which is by definition self truediv(other[, level, fill_value, axis]) Floating division of series and other, element-wise (binary operator truediv). truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular index value. tshift([periods, freq, axis]) Shift the time index, using the index’s frequency if available. tz_convert(tz[, axis, level, copy]) Convert tz-aware axis to target time zone. tz_localize(tz[, axis, level, copy, ambiguous]) Localize tz-naive TimeSeries to target time zone. unique() Return unique values in the object. unstack([level, fill_value]) 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, numeric_only]) 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, drop_level]) Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4