How to plot graphs using pandas in python | df.plot() function

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In this article, you will learn how to plot graphs using pandas in python | df.plot() function.

import pandas as pd data = [[1000,1],[1200,2],[2000,3]] df = pd.DataFrame(data,columns=['SalePrice','Bedrooms']) df.plot('Bedrooms', 'SalePrice')

You can checkout the full syntax and full parameters about this function from here.

Syntax: 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)[source]
Make plots of DataFrame using matplotlib / pylab.

data : DataFrame
x : label or position, default None
y : label, position or list of label, positions, 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

axes : matplotlib.axes.Axes or numpy.ndarray of them


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