`theta`

in `plt.polar(theta, r)`

needs to be in radians. You can make a new column converting the angle to radians using the following:

```
import math
df['rad'] = df.apply(lambda row: row.angle * math.pi / 180, axis = 1)
plt.polar(df['rad'], df['value'])
```

I'm trying to plot sensor data, which was anycodings_python recorded with different angles.,theta in plt.polar(theta, r) needs to be anycodings_matplotlib in radians. You can make a new column anycodings_matplotlib converting the angle to radians using anycodings_matplotlib the following:,But I need a plot that shows a value of 100 anycodings_python at zero degrees, 97.5 at 5 degrees and so anycodings_python on.,How do I delete rows not starting with 'x' in Pandas or keep rows starting with 'x'

I'm trying to plot sensor data, which was anycodings_python recorded with different angles.

```
import pandas as pd
import matplotlib.pyplot as plt
#create dataframe, each row contains an angle and a corresponding value
df = pd.DataFrame(columns = ["angle", "value"])
df.angle = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60]
df['value'] = df.apply(lambda row: 100 - row.angle / 2, axis = 1)
print(df)
#plotting
plt.polar(df['angle'].values, df['value'].values)
plt.show()
```

theta in plt.polar(theta, r) needs to be anycodings_matplotlib in radians. You can make a new column anycodings_matplotlib converting the angle to radians using anycodings_matplotlib the following:

```
import math
df['rad'] = df.apply(lambda row: row.angle * math.pi / 180, axis = 1)
plt.polar(df['rad'], df['value'])
```

Last Updated : 22 Jun, 2020

**Matplotlib**:*Matplotlib*is a comprehensive Python library for creating static and interactive plots and visualisations. To install this module type the below command in the terminal.

pip install matplotlib

**Numpy**:*Numpy*is the core library for array computing in Python. To install this module type the below command in the terminal.

pip install numpy

The use of the following functions, methods, classes and modules is shown in this example:,Demo of a line plot on a polar axis., Controlling the position and size of colorbars with Inset Axes ,matplotlib.projections.polar.PolarAxes.set_rmax

import numpy as np import matplotlib.pyplot as plt r = np.arange(0, 2, 0.01) theta = 2 * np.pi * r fig, ax = plt.subplots(subplot_kw = { 'projection': 'polar' }) ax.plot(theta, r) ax.set_rmax(2) ax.set_rticks([0.5, 1, 1.5, 2]) # Less radial ticks ax.set_rlabel_position(-22.5) # Move radial labels away from plotted line ax.grid(True) ax.set_title("A line plot on a polar axis", va = 'bottom') plt.show()

pyplot provides a procedural interface to the matplotlib object-oriented plotting library. It is modeled closely after Matlab™. Therefore, the majority of plotting commands in pyplot have Matlab™ analogs with similar arguments. Important commands are explained with interactive examples.,Ticks are now properly placed but their label is not very explicit. We could guess that 3.142 is but it would be better to make it explicit. When we set tick values, we can also provide a corresponding label in the second argument list. Note that we’ll use latex to allow for nice rendering of the label.,The settings have been explicitly set to their default values, but now you can interactively play with the values to explore their affect (see Line properties and Line styles below).,First step, we want to have the cosine in blue and the sine in red and a slighty thicker line for both of them. We’ll also slightly alter the figure size to make it more horizontal.

`In[1]: % matplotlib`

% matplotlib inline

```
from matplotlib
import pyplot as plt
```

```
import numpy as np
X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)
```

$ ipython--matplotlib

IPython 0.13--An enhanced Interactive Python. ? - > Introduction to IPython 's features. % magic - > Information about IPython 's ' magic ' % functions. help - > Python 's own help system. object ? - > Details about 'object'. ? object also works, ?? prints more.

This type of flexibility in the plt.plot function allows for a wide variety of possible visualization options. For a full description of the options available, refer to the plt.plot documentation.,Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables. We have seen that this is relatively straightforward in Matplotlib (Figure 4-113):,Matplotlib’s API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a lot of boilerplate code., Matplotlib’s API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a lot of boilerplate code.

So, for example, you may have a file called *myplot.py* containing
the following:

```
#-- -- -- - file: myplot.py-- -- --
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x))
plt.show()
```

You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed:

$ python myplot.py

It can be very convenient to use Matplotlib interactively within an
IPython shell (see
Chapter 1). IPython is built to work well with Matplotlib if you specify
Matplotlib mode. To enable this mode, you can use the `%matplotlib`

magic command after starting `ipython`

:

```
In[1]: % matplotlib
Using matplotlib backend: TkAgg
In[2]: import matplotlib.pyplot as plt
```

For this book, we will generally opt for `%matplotlib inline`

:

`In[3]: % matplotlib inline`

After you run this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic (Figure 4-1):

```
In[4]: import numpy as np
x = np.linspace(0, 10, 100)
fig = plt.figure()
plt.plot(x, np.sin(x), '-')
plt.plot(x, np.cos(x), '--');
```

One nice feature of Matplotlib is the ability to save figures in a wide
variety of formats. You can save a figure using the `savefig()`

command. For example, to save the previous figure as a PNG file, you can
run this:

`In[5]: fig.savefig('my_figure.png')`

We now have a file called *my_figure.png* in the current working
directory:

`In[6]: !ls - lh my_figure.png`

`In`

`[`

`6`

`]:`

`!`

`ls`

`-`

`lh`

`my_figure`

`.`

`png`

`-rw - r--r--1 jakevdp staff 16 K Aug 11 10: 59 my_figure.png`

In `savefig()`

, the file format is inferred from the extension of the
given filename. Depending on what backends you have installed, many
different file formats are available. You can find the list of supported file types for your system by using the following method of the figure
`canvas`

object:

`In[8]: fig.canvas.get_supported_filetypes()`

`In`

`[`

`8`

`]:`

`fig`

`.`

`canvas`

`.`

`get_supported_filetypes`

`()`

```
Out[8]: {
'eps': 'Encapsulated Postscript',
'jpeg': 'Joint Photographic Experts Group',
'jpg': 'Joint Photographic Experts Group',
'pdf': 'Portable Document Format',
'pgf': 'PGF code for LaTeX',
'png': 'Portable Network Graphics',
'ps': 'Postscript',
'raw': 'Raw RGBA bitmap',
'rgba': 'Raw RGBA bitmap',
'svg': 'Scalable Vector Graphics',
'svgz': 'Scalable Vector Graphics',
'tif': 'Tagged Image File Format',
'tiff': 'Tagged Image File Format'
}
```