Understanding and Solving the Repeated x Values Issue on Pandas Plot

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In this article, we will delve into a common issue that arises when creating plots using pandas and matplotlib libraries in Python. We’ll explore the problem, understand its root cause, and discuss potential solutions with code examples.

Problem Statement

We have a dataset containing information about machines that were used on different days. The goal is to create a bar chart displaying the unique values per machine per day. However, instead of having distinct dates on the x-axis, we get repeated values for each day.

Sample Dataset

Let’s first take a look at our sample dataset:

my_df = pd.DataFrame({'id':[1,2,3,4,5,6,7,8],
                      'date':['2019-01-01 07:59:54','2019-01-01 08:00:07','2019-01-01 08:00:07',
                              '2019-01-02 08:00:14','2019-01-02 08:00:16','2019-01-02 08:00:24',
                              '2019-01-03 08:02:38','2019-01-03 08:50:14'],
                      'machine':['A','A','B','C','B','C','D','D']})
my_df['date'] = pd.to_datetime(mydf['date'], infer_datetime_format=True)
my_df

Expected Output

We expect the x-axis to display three unique dates (January 1st, January 2nd, and January 3rd), each corresponding to a distinct day.

Root Cause of the Issue

The root cause of this problem lies in how we’re grouping our data for plotting. Specifically, we’re using my_df['date'].dt.date as the key for our groupby operation. This creates a new Series that contains only the date without the time component, resulting in duplicate dates.

Solution 1: Using pd.Grouper

To solve this issue, we need to use a different approach when specifying the key for our groupby operation. We can achieve this by using pd.Grouper from pandas library. The freq='D' argument specifies that we want to group by day.

fig, ax = plt.subplots(figsize=(12,6))
my_df.groupby(pd.Grouper(key='date', freq='D'))['machine'].nunique().plot(ax=ax)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %m'))
plt.show()

Solution 2: Using Time Series Resampling

Alternatively, we can also resample our data using the resample function from pandas library. This approach achieves the same result without having to manually specify the key for the groupby operation.

fig, ax = plt.subplots(figsize=(12,6))
my_df.resample('D')['machine'].nunique().plot(ax=ax)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %m'))
plt.show()

Additional Considerations

When working with time series data and grouping by specific intervals (such as days or hours), it’s essential to consider the potential impact of resampling on your analysis. For example, if you’re working with a large dataset and need to capture all unique values for each day, using resample might lead to slight discrepancies due to rounding.

Conclusion

In conclusion, we’ve discussed how to address the repeated x value issue when creating plots using pandas and matplotlib libraries in Python. By leveraging pd.Grouper or resample, we can effectively group our data by specific time intervals and achieve the desired results.

Future Improvements

There are several areas for future improvement:

We could explore how to apply these techniques to more complex datasets, including those with multiple variables or multiple dates.
Investigating ways to incorporate additional visualization tools or libraries, such as Seaborn or Plotly, might lead to enhanced visualizations and insights.
Developing a deeper understanding of the underlying algorithms and data structures used by pandas and matplotlib could help improve our ability to manipulate and analyze large datasets.

If you’re interested in learning more about pandas, matplotlib, or Python programming in general, here are some recommended topics:

Data Wrangling with Pandas
Advanced Plotting with Matplotlib
Time Series Analysis and Visualization

Last modified on 2023-08-16