Understanding the Issue with Scatterplot Creation Using Dates and Int Values

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Creating a scatterplot using dates and int values can be challenging due to differences in data types and how they are interpreted by various libraries such as pandas, seaborn, and matplotlib. In this article, we will explore the problem presented in the Stack Overflow post and provide step-by-step solutions to create an effective scatterplot.

Background Information

When working with dates and int values, it’s essential to understand that these data types have different characteristics and limitations. Dates are typically stored as strings or datetime objects, while integers represent numerical values. When creating a scatterplot, we need to consider how these data types interact with the plotting libraries.

The Problem Presented

In the Stack Overflow post, the user is attempting to create a scatterplot using the ’timestamp’ column (which contains dates) and the ‘cnt’ column (which contains int values). However, the code throws an error due to incorrect data type handling. Specifically, the line x = (df2.loc[df2['timestamp'].str.startswith('2015')]) selects the entire dataframe instead of a single column, and y = df2['cnt'] is not used correctly.

Correcting Data Type Handling

To fix this issue, we need to convert the ’timestamp’ column to a datetime dtype using pandas’ pd.to_datetime() function. This will ensure that the dates are interpreted correctly by matplotlib.

Converting Datetime to DataFrame

df2['timestamp'] = pd.to_datetime(df2['timestamp'])

This line of code converts the ’timestamp’ column to a datetime dtype, which allows matplotlib to correctly position and format the x-ticks.

Selecting Data for Scatterplot Creation

Next, we need to select the data that corresponds to the year 2015. We can do this by filtering the dataframe using the dt.year attribute, which extracts the year from the datetime object.

Filtering DataFrame

df_2015 = df2[df2['timestamp'].dt.year.eq(2015)]

This line of code filters the dataframe to include only rows where the ’timestamp’ column corresponds to the year 2015. The resulting dataframe, df_2015, is then used for scatterplot creation.

Creating Scatterplot

To create the scatterplot, we use the plot() function from pandas, which uses matplotlib as the default plotting backend. We specify the ’timestamp’ column as the x-values and the ‘cnt’ column as the y-values.

Creating Scatterplot

ax = df_2015.plot(x='timestamp', marker='.', ls='')

This line of code creates the scatterplot, using the ’timestamp’ column as the x-axis and the ‘cnt’ column as the y-axis. The marker='.' argument specifies that we want to use a single point marker, while ls='' removes any line style.

Formatting X-Ticks and Labels

The x-ticks and labels will be formatted depending on the range of the data. We can change the formatting using various options available in matplotlib.

Changing Formatting

To change the formatting of a datetime axis, we can use the DateFormatter class from matplotlib’s dates module.

import matplotlib.dates as mdates

ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))

This line of code formats the x-axis tick labels to display only the year.

Setting X-Axis Limits

We can set the x-axis limits using the xlim() function from matplotlib.

ax.set_xlim(df_2015['timestamp'].min(), df_2015['timestamp'].max())

This line of code sets the x-axis limits to include only the dates between the minimum and maximum values in the ’timestamp’ column.

Conclusion

Creating a scatterplot using dates and int values requires careful consideration of data type handling, selection, and formatting. By following the steps outlined in this article, you can create an effective scatterplot that accurately represents your data.

Example Use Case

Here’s an example use case that demonstrates how to create a scatterplot using the ’timestamp’ column (dates) and the ‘cnt’ column (int values):

import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
from datetime import datetime
import numpy as np

# Load sample dataframe
data = {'timestamp': ['2015-01-04', '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08', 
                   '2016-12-27', '2016-12-28', '2016-12-29', '2016-12-30', '2016-12-31'],
        'cnt': [9234, 20372, 20613, 21064, 15601, 10842, 12428, 14052, 11566, 11424]}
df2 = pd.DataFrame(data)

# Convert timestamp to datetime dtype
df2['timestamp'] = pd.to_datetime(df2['timestamp'])

# Filter dataframe for year 2015
df_2015 = df2[df2['timestamp'].dt.year.eq(2015)]

# Create scatterplot
ax = df_2015.plot(x='timestamp', marker='.', ls='')

# Set x-axis limits
ax.set_xlim(df_2015['timestamp'].min(), df_2015['timestamp'].max())

# Show plot
plt.show()

This code creates a scatterplot using the ’timestamp’ column (dates) and the ‘cnt’ column (int values), with the x-axis limits set to include only the dates between January 1, 2015, and December 31, 2015.

Last modified on 2024-07-27