The slope equation $y=mx+c$ as we know it today is attributed to René Descartes (AD 1596-1650), Father of Analytic Geometry. Table of Contents The equation $y=mx+c$ represents a straight line graphically, where $m$ is its slope/gradient and $c$ its intercept. In this tutorial, you will learn how to plot $y=mx+b$ in Python with Matplotlib. Consider the straight line $y=2x+1$, whose slope/gradient is $2$ and intercept is $1$. Before we plot, we need to import NumPy and use its There are many other line-styles available in Matplotlib besides And the same goes for the colour. Below you can check out the remaining basic in-built colours.
Positioning the Axes at the Centre When we plot a line with slope and intercept, we usually/traditionally position the axes at the middle of the graph. In the below code, we move the left and bottom spines to the center of the graph applying Multiple Straight LinesWe now plot multiple lines in the same graph, positioning the axes at the centre. Plot y versus x as lines and/or markers. Call signatures: plot([x], y, [fmt], *, data=None, **kwargs) plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) The coordinates of the points or line nodes are given by x, y. The optional parameter fmt is a convenient way for defining basic formatting like color, marker and linestyle. It's a shortcut string notation described in the Notes section below. >>> plot(x, y) # plot x and y using default line style and color >>> plot(x, y, 'bo') # plot x and y using blue circle markers >>> plot(y) # plot y using x as index array 0..N-1 >>> plot(y, 'r+') # ditto, but with red plusses You can use >>> plot(x, y, 'go--', linewidth=2, markersize=12) >>> plot(x, y, color='green', marker='o', linestyle='dashed', ... linewidth=2, markersize=12) When conflicting with fmt, keyword arguments take precedence. Plotting labelled data There's a convenient way for plotting objects with labelled
data (i.e. data that can be accessed by index >>> plot('xlabel', 'ylabel', data=obj) All indexable objects are supported. This could e.g. be a Plotting multiple sets of data There are various ways to plot multiple sets of data.
By default, each line is assigned a different style specified by a 'style cycle'. The fmt and line property parameters are only necessary if you want explicit deviations from these defaults. Alternatively, you can also change the style cycle using The horizontal / vertical coordinates of the data
points. x values are optional and default to Commonly, these parameters are 1D arrays. They can also be scalars, or two-dimensional (in that case, the columns represent separate data sets). These arguments cannot be passed as keywords. fmtstr, optionalA format string, e.g. 'ro' for red circles. See the Notes section for a full description of the format strings. Format strings are just an abbreviation for quickly setting basic line properties. All of these and more can also be controlled by keyword arguments. This argument cannot be passed as keyword. dataindexable object, optionalAn object with labelled data. If given, provide the label names to plot in x and y. Note Technically there's a slight ambiguity in calls where the second label is a valid fmt. Line2D A list of lines representing the plotted data. Other Parameters:scalex, scaleybool, default: TrueThese parameters determine if the view limits are adapted to the data limits. The values are passed on to Line2D properties, optionalkwargs are used to specify properties like a line label (for auto legends), linewidth, antialiasing, marker face color. Example: >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2) >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2') If you specify multiple lines with one plot call, the kwargs apply to all those lines. In case the label object is iterable, each element is used as labels for each set of data. Here is a list of available
See also scatter XY scatter plot with markers of varying size and/or color ( sometimes also called bubble chart). Notes Format Strings A format string consists of a part for color, marker and line: fmt = '[marker][line][color]' Each of them is optional. If not provided, the value from the style cycle is used. Exception: If Other combinations such as Markers
Line Styles
Example format strings: 'b' # blue markers with default shape 'or' # red circles '-g' # green solid line '--' # dashed line with default color '^k:' # black triangle_up markers connected by a dotted line Colors The supported color abbreviations are the single letter codes
and the If the color is the only part of the format string, you can additionally use any Examples using matplotlib.pyplot.plot#How do you make a line plot in Python?Simple Line Plots. %matplotlib inline import matplotlib.pyplot as plt plt. style. use('seaborn-whitegrid') import numpy as np. ... . fig = plt. figure() ax = plt. axes() ... . In [3]: fig = plt. figure() ax = plt. ... . In [4]: plt. plot(x, np. ... . In [5]: plt. plot(x, np. ... . plt. plot(x, x + 0, '-g') # solid green plt. ... . In [9]: plt. ... . In [10]: plt.. What is the use of line plot in Python?A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. These are also used to express trend in data over intervals of time. x and y are arrays. How should you create a line plot using matplotlib?Steps to Plot a Line Chart in Python using Matplotlib. Step 1: Install the Matplotlib package. ... . Step 2: Gather the data for the Line chart. ... . Step 3: Capture the data in Python. ... . Step 4: Plot a Line chart in Python using Matplotlib.. |