Multiple columns visualization with plotly or seaborn

Question

I have data of factories and their error codes during production such as below;

PlantID  A  B  C  D
1        0  1  2  4
1        3  0  2  0
3        0  0  0  1
4        0  1  1  5

Each row represent production order. I want to create a graph with x-axis=PlantID's and y-axis are A,B,C,D with different bars. In this way I can see that which factory has the most D error, which has A in one graph

I usually use plotly and seaborn but I couldn't find any solution for that, y-axis is single column in every example

Thanks in advance,

Community · Accepted Answer · 2020-06-20 09:12:55Z

Seaborn likes its data in long or wide-form.

As mentioned above, seaborn will be most powerful when your datasets have a particular organization. This format ia alternately called “long-form” or “tidy” data and is described in detail by Hadley Wickham in this academic paper. The rules can be simply stated:

Each variable is a column

Each observation is a row

The following code converts the original dataframe to a long form dataframe. By stacking the columns on top of each other such that every row corresponds to a single record that specifies the column name and the value (the count).

import numpy as np
import pandas as pd
import seaborn as sns
# Generating some data
N = 20
PlantID = np.random.choice(np.arange(1, 4), size=N, replace=True)
data = dict((k, np.random.randint(0, 50, size=N)) for k in ['A', 'B', 'C', 'D'])
df = pd.DataFrame(data, index=PlantID)
df.index = df.index.set_names('PlantID')
# Stacking the columns and resetting the index to create a longformat. (And some renaming)
df = df.stack().reset_index().rename({'level_1' : 'column', 0: 'count'},axis=1)
sns.barplot(x='PlantID', y='count', hue='column', data=df)

Yaakov Bressler · Accepted Answer · 2019-07-26 12:37:30Z

0

Pandas has really clever built-in plotting functionality:

df.plot(kind='bar')
plt.show()

answered Jul 26, 2019 at 12:37

Yaakov Bressler

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