feat: added normal plot for time series

src/safeds/data/tabular/containers/_time_series.py

@@ -6,6 +6,7 @@
 
 import matplotlib.pyplot as plt
 import pandas as pd
+import seaborn as sns
 
 from safeds.data.image.containers import Image
 from safeds.data.tabular.containers import Column, Row, Table, TaggedTable
@@ -36,7 +37,7 @@ def _from_tagged_table(
 
  Parameters
  ----------
- table : TaggedTable
+ tagged_table: TaggedTable
  The tagged table.
  time_name: str
  Name of the time column.
@@ -906,3 +907,150 @@ def plot_lagplot(self, lag: int) -> Image:
  plt.close() # Prevents the figure from being displayed directly
  buffer.seek(0)
  return Image.from_bytes(buffer.read())
+
+ def plot_lineplot(self, x_column_name: str | None = None, y_column_name: str | None = None) -> Image:
+ """
+
+ Plot the time series target or the given column(s) as line plot.
+
+ The function will take the time column as the default value for y_column_name and the target column as the
+ default value for x_column_name.
+
+ Parameters
+ ----------
+ x_column_name:
+ The column name of the column to be plotted on the x-Axis, default is the time column.
+ y_column_name:
+ The column name of the column to be plotted on the y-Axis, default is the target column.
+
+ Returns
+ -------
+ plot:
+ The plot as an image.
+
+ Raises
+ ------
+ NonNumericColumnError
+ If the time series given columns contain non-numerical values.
+
+ UnknownColumnNameError
+ If one of the given names does not exist in the table
+
+ Examples
+ --------
+ >>> from safeds.data.tabular.containers import TimeSeries
+ >>> table = TimeSeries({"time":[1, 2], "target": [3, 4], "feature":[2,2]}, target_name= "target", time_name="time", feature_names=["feature"], )
+ >>> image = table.plot_lineplot()
+
+ """
+ self._data.index.name = "index"
+ if x_column_name is not None and not self.get_column(x_column_name).type.is_numeric():
+ raise NonNumericColumnError("The time series plotted column contains non-numerical columns.")
+
+ if y_column_name is None:
+ y_column_name = self.target.name
+
+ elif y_column_name not in self._data.columns:
+ raise UnknownColumnNameError([y_column_name])
+
+ if x_column_name is None:
+ x_column_name = self.time.name
+
+ if not self.get_column(y_column_name).type.is_numeric():
+ raise NonNumericColumnError("The time series plotted column contains non-numerical columns.")
+
+ fig = plt.figure()
+ ax = sns.lineplot(
+ data=self._data,
+ x=x_column_name,
+ y=y_column_name,
+ )
+ ax.set(xlabel=x_column_name, ylabel=y_column_name)
+ ax.set_xticks(ax.get_xticks())
+ ax.set_xticklabels(
+ ax.get_xticklabels(),
+ rotation=45,
+ horizontalalignment="right",
+ ) # rotate the labels of the x Axis to prevent the chance of overlapping of the labels
+ plt.tight_layout()
+
+ buffer = io.BytesIO()
+ fig.savefig(buffer, format="png")
+ plt.close() # Prevents the figure from being displayed directly
+ buffer.seek(0)
+ self._data = self._data.reset_index()
+ return Image.from_bytes(buffer.read())
+
+ def plot_scatterplot(
+ self,
+ x_column_name: str | None = None,
+ y_column_name: str | None = None,
+ ) -> Image:
+ """
+ Plot the time series target or the given column(s) as scatter plot.
+
+ The function will take the time column as the default value for x_column_name and the target column as the
+ default value for y_column_name.
+
+ Parameters
+ ----------
+ x_column_name:
+ The column name of the column to be plotted on the x-Axis.
+ y_column_name:
+ The column name of the column to be plotted on the y-Axis.
+
+ Returns
+ -------
+ plot:
+ The plot as an image.
+
+ Raises
+ ------
+ NonNumericColumnError
+ If the time series given columns contain non-numerical values.
+
+ UnknownColumnNameError
+ If one of the given names does not exist in the table
+
+ Examples
+ --------
+ >>> from safeds.data.tabular.containers import TimeSeries
+ >>> table = TimeSeries({"time":[1, 2], "target": [3, 4], "feature":[2,2]}, target_name= "target", time_name="time", feature_names=["feature"], )
+ >>> image = table.plot_scatterplot()
+
+ """
+ self._data.index.name = "index"
+ if x_column_name is not None and not self.get_column(x_column_name).type.is_numeric():
+ raise NonNumericColumnError("The time series plotted column contains non-numerical columns.")
+
+ if y_column_name is None:
+ y_column_name = self.target.name
+ elif y_column_name not in self._data.columns:
+ raise UnknownColumnNameError([y_column_name])
+ if x_column_name is None:
+ x_column_name = self.time.name
+
+ if not self.get_column(y_column_name).type.is_numeric():
+ raise NonNumericColumnError("The time series plotted column contains non-numerical columns.")
+
+ fig = plt.figure()
+ ax = sns.scatterplot(
+ data=self._data,
+ x=x_column_name,
+ y=y_column_name,
+ )
+ ax.set(xlabel=x_column_name, ylabel=y_column_name)
+ ax.set_xticks(ax.get_xticks())
+ ax.set_xticklabels(
+ ax.get_xticklabels(),
+ rotation=45,
+ horizontalalignment="right",
+ ) # rotate the labels of the x Axis to prevent the chance of overlapping of the labels
+ plt.tight_layout()
+
+ buffer = io.BytesIO()
+ fig.savefig(buffer, format="png")
+ plt.close() # Prevents the figure from being displayed directly
+ buffer.seek(0)
+ self._data = self._data.reset_index()
+ return Image.from_bytes(buffer.read())