Understanding the Plot Data to Line Chart Error in Python/Pandas

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In this article, we’ll delve into the error caused by plotting data from a SQL stored procedure using Python and Pandas. We’ll explore why converting an object data type to datetime doesn’t work as expected and how to solve the issue.

Introduction

As developers, we often need to connect our applications to external data sources, such as databases or APIs, to fetch relevant information. When working with SQL stored procedures, it’s common to encounter data types that don’t match the expected format for plotting. In this article, we’ll focus on a specific error related to plotting data from an object data type using Pandas.

The Error

The original code attempts to connect to a SQL database using PyODBC and fetch data from a stored procedure:

import pyodbc
import pandas as pd

server = 'SERVER'
database = 'DATABASE'
username = 'USERNAME'
password = 'PASSWORD'

sql_connect = pyodbc.connect(
    'DRIVER={SQL Server};SERVER=' + server + ';DATABASE=' + database + ';UID=' + username + ';PWD=' + password
)

data = pd.read_sql('EXECUTE [dbo].[stored_procedure]', sql_connect)

After fetching the data, we try to create a line chart using Matplotlib:

import matplotlib.pyplot as plt

df = pd.DataFrame({
    '3000': data["3000"],
    '4000': data["4000"],
    '5000': data["5000"],
    '6000': data["6000"],
    '7000': data["7000"],
    '8000': data["8000"],
    '8200': data["8200"],
}, index=data.Hour)
chart = df.plot.line()

However, when we run this code, we encounter the following error:

ValueError: Could not convert object to datetime

This error occurs because the Hour column is an object data type, which cannot be converted to a datetime format.

The Solution

To solve this issue, we need to convert the Hour column to a datetime format. We can use the pd.to_datetime() function with the correct date and time format.

Here’s the modified code:

import pandas as pd
import matplotlib.pyplot as plt

server = 'SERVER'
database = 'DATABASE'
username = 'USERNAME'
password = 'PASSWORD'

sql_connect = pyodbc.connect(
    'DRIVER={SQL Server};SERVER=' + server + ';DATABASE=' + database + ';UID=' + username + ';PWD=' + password
)

data = pd.read_sql('EXECUTE [dbo].[stored_procedure]', sql_connect)
df = data[['3000', '4000', '5000', '6000', '7000', '8000', '8200']]

# Convert Hour column to datetime format
df['Hour'] = pd.to_datetime(df['Hour'], format='%H:%M')

chart = df.plot.line(
    color={
        "3000": "#00FFFF",
        "4000": "#228B22",
        "5000": "#191970",
        "6000": "#CC5500",
        "7000": "#0D98BA",
        "8000": "#FFFF00",
        "8200": '#FF69B4'
    }
)
plt.show()
sql_connect.close()

In this modified code, we first select the relevant columns from the original data frame. Then, we convert the Hour column to a datetime format using the pd.to_datetime() function with the correct format ('%H:%M') and assign it back to the df data frame.

With this modification, the line chart should display correctly, showcasing the data points in chronological order.

Additional Tips

When working with SQL stored procedures and Python/Pandas, keep the following tips in mind:

Always check the data types of your variables and ensure they match the expected format for plotting.
Use pd.to_datetime() to convert datetime columns to a format that can be plotted using Matplotlib or Seaborn.
When working with large datasets, consider using Pandas’ built-in functions for efficient data manipulation and analysis.

By following these tips and understanding how to handle object data types in Python/Pandas, you’ll be better equipped to tackle common challenges when connecting your applications to external data sources.

Last modified on 2024-07-13