# pandas.DataFrame.plot

`DataFrame.``plot`(x=None, y=None, kind='line', ax=None, subplots=False, sharex=None, sharey=False, layout=None, figsize=None, use_index=True, title=None, grid=None, legend=True, 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, secondary_y=False, sort_columns=False, **kwds)

  Make plots of DataFrame using matplotlib / pylab.

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

  Parameters:

  data : DataFrame

  x : label or position, default None

  y : label or position, default None

  > Allows plotting of one column versus another

  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
  > - ‘scatter’ : scatter plot
  > - ‘hexbin’ : hexbin plot

  ax : matplotlib axes object, default None

  subplots : boolean, default False

  > Make separate subplots for each column

  sharex : boolean, default True if ax is None else False

  > In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and sharex=True will alter all x axis labels for all axis in a figure!

  sharey : boolean, default False

  > In case subplots=True, share y axis and set some y axis labels to invisible

  layout : tuple (optional)

  > (rows, columns) for the layout of subplots

  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.

  stacked : boolean, default False in line and

  > bar plots, and True in area plot. If True, create stacked plot.

  sort_columns : boolean, default False

  > Sort column names to determine plot ordering

  secondary_y : boolean or sequence, default False

  > Whether to plot on the secondary y-axis If a list/tuple, which columns to plot on secondary y-axis

  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)
  - If kind = ‘scatter’ and the argument c is the name of a dataframe column, the values of that column are used to color each point.
  - If kind = ‘hexbin’, you can control the size of the bins with the gridsize argument. By default, a histogram of the counts around each (x, y) point is computed. You can specify alternative aggregations by passing values to the C and reduce_C_function arguments. C specifies the value at each (x, y) point and reduce_C_function is a function of one argument that reduces all the values in a bin to a single number (e.g. mean, max, sum, std).

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