Install and use libraries in Python

Business Benefits

Build modules and functions for data science, manipulation, and visualization.

Install the most commonly used Python libraries: Pandas, NumPy, Plotly, and Dash.

  • Use Pandas for analysis on data that’s in a table format. Install Pandas in your terminal:
    > python3 -m pip install pandas
  • Use NumPy to quickly generate data arrays and matrices. Install NumPy in your terminal:
    > python3 -m pip install numpy
  • Use Plotly - - to create charts like scatter plots, line charts, bar charts, and bubble charts. Install Plotly in your terminal:
    > python3 -m pip install plotly==4.14.1
    > python3 -m pip install xlrd
  • Use Dash to create full dashboards with multiple components, interactivity, and multiple plots. Install Dash in your terminal:
    > python3 -m pip install dash

Use the DataFrame() class to create and initialize DataFrames using Pandas.

The DataFrame() class syntax is:

DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)

```For example, to create and initialize a DataFrame from a list of lists, you would pass this list of lists to the `pandas.DataFrame()` constructor as a `data` argument.

import pandas as pd

#list of lists
data = [['a1', 'b1', 'c1'],
        ['a2', 'b2', 'c2'],
        ['a3', 'b3', 'c3']]

df = pd.DataFrame(data)
## Create DataFrames by passing the **Numpy** `array` as a data parameter to `pandas.DataFrame()`.

For example, using the code below, you can:

- Import the Pandas package and Numpy package.
- Initialize the 2D Numpy `array`.
- Use the `pandas.DataFrame(numpy array)` to return the DataFrame.

import pandas as pd
import numpy as np

array = np.array([['a1', 'b1', 'c1'],
                  ['a2', 'b2', 'c2'],
                  ['a3', 'b3', 'c3']])
df = pd.DataFrame(array)

    0   1   2
0  a1  b1  c1
1  a2  b2  c2
2  a3  b3  c3
## Create a visualization bar chart from a CSV file using Plotly.

For example, if you created a CSV file with the fields ***ID***, ***Customer Name***, ***Order Number***, ***Order Amount***, ***Date Ordered***, and ***Date Delivered***, you would:

- Call the CSV file using the Pandas library and output the sheet using Pandas.
- Create a bar chart using the above CSV file with ***Customer Name*** as the x-axis and as ***Order Amount*** as the y-axis.

import pandas as pd
import plotly.offline as pyo
import plotly.graph_objs as go
data = [go.Bar(x=df['Customer Name'],y=df['Order Amount'])]
layout = go.Layout(title='Customer Order Data')
fig = go.Figure(data=data,layout=layout)


## Use Dash to create line charts and include parameters for users to choose their desired output.

For example, using Dash, you could import an open source library of data that consists of life expectancy by country. Next, you can create the line charts using the data and color code the output to display the life expectancy by country, and parameters for users to choose the continents:

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import as px

df =
all_continents = df.continent.unique()

app = dash.Dash(__name__)

app.layout = html.Div([
        options=[{"label": x, "value": x}
                 for x in all_continents],
        labelStyle={'display': 'inline-block'}

    Output("line-chart", "figure"),
    [Input("checklist", "value")])
def update_line_chart(continents):
    mask = df.continent.isin(continents)
    fig = px.line(df[mask],
        x="year", y="lifeExp", color='country')
    return fig