# pandas.Series.plot

`Series.``plot`(kind='line', ax=None, figsize=None, use_index=True, title=None, grid=None, legend=False, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, label=None, secondary_y=False, **kwds)

  Make plots of Series using matplotlib / pylab.

  New in version 0.17.0: Each plot kind has a corresponding method on the `Series.plot` accessor: `s.plot(kind='line')` is equivalent to `s.plot.line()`.

  Parameters:

  data : Series

  kind : str

  > - ‘line’ : line plot (default)
  > - ‘bar’ : vertical bar plot
  > - ‘barh’ : horizontal bar plot
  > - ‘hist’ : histogram
  > - ‘box’ : boxplot
  > - ‘kde’ : Kernel Density Estimation plot
  > - ‘density’ : same as ‘kde’
  > - ‘area’ : area plot
  > - ‘pie’ : pie plot

  ax : matplotlib axes object

  > If not passed, uses gca()

  figsize : a tuple (width, height) in inches

  use_index : boolean, default True

  > Use index as ticks for x axis

  title : string or list

  > Title to use for the plot. If a string is passed, print the string at the top of the figure. If a list is passed and subplots is True, print each item in the list above the corresponding subplot.

  grid : boolean, default None (matlab style default)

  > Axis grid lines

  legend : False/True/’reverse’

  > Place legend on axis subplots

  style : list or dict

  > matplotlib line style per column

  logx : boolean, default False

  > Use log scaling on x axis

  logy : boolean, default False

  > Use log scaling on y axis

  loglog : boolean, default False

  > Use log scaling on both x and y axes

  xticks : sequence

  > Values to use for the xticks

  yticks : sequence

  > Values to use for the yticks

  xlim : 2-tuple/list

  ylim : 2-tuple/list

  rot : int, default None

  > Rotation for ticks (xticks for vertical, yticks for horizontal plots)

  fontsize : int, default None

  > Font size for xticks and yticks

  colormap : str or matplotlib colormap object, default None

  > Colormap to select colors from. If string, load colormap with that name from matplotlib.

  colorbar : boolean, optional

  > If True, plot colorbar (only relevant for ‘scatter’ and ‘hexbin’ plots)

  position : float

  > Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)

  table : boolean, Series or DataFrame, default False

  > If True, draw a table using the data in the DataFrame and the data will be transposed to meet matplotlib’s default layout. If a Series or DataFrame is passed, use passed data to draw a table.

  yerr : DataFrame, Series, array-like, dict and str

  > See Plotting with Error Bars for detail.

  xerr : same types as yerr.

  label : label argument to provide to plot

  secondary_y : boolean or sequence of ints, default False

  > If True then y-axis will be on the right

  mark_right : boolean, default True

  > When using a secondary_y axis, automatically mark the column labels with “(right)” in the legend

  kwds : keywords

  > Options to pass to matplotlib plotting method

  Returns: | axes : matplotlib.AxesSubplot or np.array of them

  Notes

  - See matplotlib documentation online for more on this subject
  - If kind = ‘bar’ or ‘barh’, you can specify relative alignments for bar plot layout by position keyword. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)

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