The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.

Bokeh’s mid-level general purpose bokeh.plotting interface is centered around two main components: data and glyphs.

The basic steps to creating plots with the bokeh.plotting interface are:

1. Prepare some data:

Python lists, NumPy arrays, Pandas DataFrames and other sequences of values

2. Create a new plot

3. Add renderers for your data, with visual customizations
 
4. Specify where to generate the output

5. Show or save the results

```
from bokeh.plotting import figure
from bokeh.io import output_file, show
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]
p = figure(title="simple line example",
p.line(x, y, legend="Temp.", line_width=2) 
           x_axis_label='x',
           y_axis_label='y')
output_file("lines.html") 
 show(p) 
```

Plotting with Bokeh

In pandas workflows, visualization is typically delegated to dedicated libraries such as matplotlib, which provide a more complete suite of plotting functions than pandas itself. Before passing data to these visualization methods, it must be cleaned properly (e.g., column names must be correct and the relevant data selected accurately), since unexpected behavior can otherwise result, especially as the dataset grows large.

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Pandas provides a large suite of functions designed for exploratory analysis and data manipulation. These include:
- Pivot Tables
- Multi-Indexing
- Merging / Concatenation
- Date-Time
- Visualization
- Sub-Sets

Exploratory Data Analysis in Pandas

Pandas provides several tools for tidying data, each covered in its own child node: selecting data (indexing/slicing DataFrames), merging DataFrames with `pandas.concat()`, joining DataFrames on a key with `pandas.merge()` (SQL-style joins), reshaping data (stacking, unstacking, pivoting, melting), handling missing values (`dropna`, `fillna`), and converting between date-time representations.

Tools for Tidying Data

Pandas is a well designed library for raw data processing and manipulation and works very well with other packages such as matplotlib and numpy. Pandas has built in read functions that work for a large variety of data file types, ranging from CSV to html to SPSS. Data that is read in will be converted to a data frame object, which can easily be converted then to other formats , i.e. from a SQL query to a CSV file. 

Usage: ```read_csv(path/filename)```

this will return a data frame object which can be stored in a variable for later use. Other file types will just require the _csv to be changed to _html etc. If the file is included in the same directory as the python program, just the filename as a string passed in as the first parameter will be sufficient. For more details visit:

https://pandas.pydata.org/docs/user_guide/io.html

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