<jupyter_start><jupyter_text>GRAPH PLOTTING A graphical representation of dataset is graph plotting. Matplotlib is very popular library.          %matplotlib inline is a magic function use for static graph with in          cell. we don't need to write plt.show() all the time.<jupyter_code>import matplotlib.pyplot as plt
import numpy as np
import ipympl
from matplotlib import style
%matplotlib inline
%matplotlib widget
plt.style.available #the list of style available in packages
#syntax: plt.style.use('style_name')
data=np.random.randn(100)
plt.style.use('ggplot')
plt.plot(data)
plt.style.use('tableau-colorblind10')
plt.plot(data)
plt.style.use('fivethirtyeight')
plt.plot([1,2,3,4,5],[6,7,8,9,10])
plt.style.use('grayscale')
x=np.random.randn(100)
#plt.hist() is a histogram which is type of bar plot that use shows the frequency or comparison
plt.hist(x, linewidth=2, edgecolor='blue')<jupyter_output><empty_output><jupyter_text>plt.hist(x,.......) , x: the sequence of data.<jupyter_code>plt.hist
pip install ipympl
conda activate ipympl-dev

mamba env create --file dev-environment.yml
jupyter nbextension enable --sys-prefix ipympl<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '61611', 'license': 'MIT', 'n_tokens_mistral': 426, 'n_tokens_neox': 398, 'n_words': 114, 'path': 'matplotlib/plottypes.ipynb', 'repo_name': 'DharmendraGITHB/DataScience', 'size': '1173'}