""" This module implements the plot(df) function. """ from typing import Any, Dict, List, Optional, Union import dask.dataframe as dd import pandas as pd from ..configs import Config from ..container import Container from ..dtypes_v2 import DTypeDef, LatLong from ...progress_bar import ProgressBar from .compute import compute from .render import render __all__ = ["plot", "compute", "render"] [docs]def plot( df: Union[pd.DataFrame, dd.DataFrame], col1: Optional[Union[str, LatLong]] = None, col2: Optional[Union[str, LatLong]] = None, col3: Optional[str] = None, *, config: Optional[Dict[str, Any]] = None, display: Optional[List[str]] = None, dtype: Optional[DTypeDef] = None, progress: bool = True, ) -> Container: """Generates plots for exploratory data analysis. If no columns are specified, the distribution of each coloumn is plotted. A histogram is plotted if the column contains numerical values, a bar chart is plotted if the column contains categorical values, a line chart is plotted if the column is of type datetime. If one column (x) is specified, the distribution of x is plotted in various ways. If x contains categorical values, a bar chart and pie chart are plotted. If x contains numerical values, a histogram, kernel density estimate plot, box plot, and qq plot are plotted. If x contains datetime values, a line chart is plotted. If two columns (x and y) are specified, plots depicting the relationship between the variables will be displayed. If x and y contain numerical values, a scatter plot, hexbin plot, and binned box plot are plotted. If one of x and y contain categorical values and the other contains numerical values, a box plot and multiline histogram are plotted. If x and y contain categorical vales, a nested bar chart, stacked bar chart, and heat map are plotted. If one of x and y contains datetime values and the other contains numerical values, a line chart and a box plot are shown. If one of x and y contains datetime values and the other contains categorical values, a multiline chart and a stacked box plot are shown. If x, y, and z are specified, they must be one each of type datetime, numerical, and categorical. A multiline chart containing an aggregate on the numerical column grouped by the categorical column over time is plotted. Parameters ---------- df DataFrame from which visualizations are generated col1: Optional[str], default None A valid column name from the dataframe col2: Optional[str], default None A valid column name from the dataframe col3: Optional[str], default None A valid column name from the dataframe config A dictionary for configuring the visualizations E.g. config={"hist.bins": 20} display A list containing the names of the visualizations to display E.g. display=["Histogram"] dtype: str or DType or dict of str or dict of DType, default None Specify Data Types for designated column or all columns. E.g. dtype = {"a": Continuous, "b": "Nominal"} or dtype = {"a": Continuous(), "b": "nominal"} or dtype = Continuous() or dtype = "Continuous" or dtype = Continuous(). progress Enable the progress bar. Examples -------- >>> import pandas as pd >>> from dataprep.eda import * >>> iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv') >>> plot(iris) >>> plot(iris, "petal_length") >>> plot(iris, "petal_width", "species") """ cfg = Config.from_dict(display, config) with ProgressBar(minimum=1, disable=not progress): itmdt = compute(df, col1, col2, col3, cfg=cfg, dtype=dtype) to_render = render(itmdt, cfg) return Container(to_render, itmdt.visual_type, cfg)