<jupyter_start><jupyter_text>Basic Time Series Visualisation<jupyter_code>import numpy as np
from numpy.random import randn
import pandas as pd
from pandas import Series, DataFrame

from pandas.tools.plotting import scatter_matrix

import matplotlib.pyplot as plt
from pylab import rcParams
import seaborn as sb
%matplotlib inline
rcParams['figure.figsize'] = 6,5
sb.set_style('whitegrid')
address = 'C:\Users\<NAME>\Desktop\Ex_Files_Python_Data_Science_EssT\superstore-sales.csv'
df = pd.read_csv(address, index_col='Order Date', parse_dates= True)
df.head(5)
df2 = df.sample(n=100, random_state=25, axis=0)

plt.xlabel('Order Date')
plt.ylabel('Order Quantity')
plt.title('Superstore Sales')

df2['Order Quantity'].plot()<jupyter_output><empty_output><jupyter_text>Seaborn<jupyter_code>address = 'C:\Users\<NAME>\Desktop\Ex_Files_Python_Data_Science_EssT\mtcars.csv'
cars = pd.read_csv(address)
cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']
mpg = cars['mpg']
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
mpg.plot(kind='hist', color = 'salmon')
ax.set_xticks(range(35))
sb.distplot(mpg, color = 'powderblue')
cars.plot(kind='scatter', x='hp', y='mpg', c=['powderblue'], s=150)
sb.regplot(x='hp', y='mpg', data=cars, scatter=True)
sb.pairplot(cars)
cars_df = pd.DataFrame((cars.ix[:,(1,3,4,6)].values), columns = ['mpg', 'disp', 'hp', 'wt'])
cars_target = cars.ix[:,9].values
target_names = [0, 1]

cars_df['group'] = pd.Series(cars_target, dtype="category")
sb.pairplot(cars_df, hue='group', palette='hls')
cars.boxplot(column='mpg', by='am')
sb.boxplot(x='am', y='mpg', data=cars, palette='hls')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18864577173270256, 'fraction_numerical': 0.01596688350088705, 'mean_word_length': 7.8125, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '434857', 'license': 'BSD-2-Clause', 'n_tokens_mistral': 703, 'n_tokens_neox': 653, 'n_words': 125, 'path': 'Data Visualisation with Python Part 2.ipynb', 'repo_name': 'kengying5/Data-Visualisation-with-Python', 'size': '1705'}