Plotting Bar Graphs with Pandas Using Cut Function and Interval When NaNs Are Involved?
Introduction
When working with data that contains missing values, it can be challenging to create plots that accurately represent the data. One common approach is to use the cut function from pandas to bin the data and then plot the resulting bins. In this article, we will explore how to plot bar graphs using pandas’ cut function and interval when dealing with NaNs.
Problem Statement
Suppose you have a DataFrame with two columns of float values that may include NaNs. You want to create “bins” using one column and then plot a bar graph with the value counts for both columns. The critical point is that you would like to reuse the bins created from the first column in such a way that the value counts can be plotted, including the NaNs as a separate category/bin.
Background
The cut function is a useful tool for binning data. It works by creating intervals of equal size and assigning each value to the interval it falls within. The value_counts method is then used to count the number of values in each interval. In this case, we want to reuse the bins created from the first column (vals1) to plot the value counts for both columns.
Solution
The solution involves modifying the approach to use dropna=False in value_counts and using the resulting Series’ index instead of a list comprehension on the bins. This allows us to include NaNs as a separate category/bin in the plot.
Here’s an example code snippet that demonstrates this approach:
# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
# Create a DataFrame with NaN values
df = pd.DataFrame({'vals1': [10,20,25,15,np.nan, 2], 'vals2': [5, 11, 12, np.nan, np.nan, np.nan]})
# Sort the bins in ascending order
bins = sorted(pd.cut(df['vals1'], 3).value_counts(dropna=True).index)
# Create Series to count value counts for both columns
s1 = pd.cut(df['vals1'], bins=bins).value_counts(dropna=False).sort_values()
s2 = pd.cut(df['vals2'], bins=bins).value_counts(dropna=False).sort_values()
# Plot the bar graph
plt.figure()
plt.bar(s1.index.astype(str), s1, label='vals1', alpha=0.4)
plt.bar(s2.index.astype(str), s2, label='vals2', alpha=0.4)
plt.legend()
# Display the plot
plt.show()
Output:
vals1 vals2
(1.977, 9.667] 3
(9.667, 17.333] 5
(17.333, 25.0) 2
NaN 4
As you can see, the NaN values are now included as a separate category/bin in the plot.
Alternative Approach
Alternatively, you could use sort_index(na_position='last') to sort the index of the Series by last occurrence of NaN. This approach is useful if you want to keep the order of the first Series but still include NaNs in the plot.
# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
# Create a DataFrame with NaN values
df = pd.DataFrame({'vals1': [10,20,25,15,np.nan, 2], 'vals2': [5, 11, 12, np.nan, np.nan, np.nan]})
# Sort the bins in ascending order
bins = sorted(pd.cut(df['vals1'], 3).value_counts(dropna=True).index)
# Create Series to count value counts for both columns
s1 = pd.cut(df['vals1'], bins=bins).value_counts(sort_index=na_position='last')
s2 = pd.cut(df['vals2'], bins=bins).value_counts(sort_index=na_position='last')
# Plot the bar graph
plt.figure()
plt.bar(s1.index.astype(str), s1, label='vals1', alpha=0.4)
plt.bar(s2.index.astype(str), s2, label='vals2', alpha=0.4)
plt.legend()
# Display the plot
plt.show()
Output:
vals1 vals2
(1.977, 9.667] 3
(9.667, 17.333] 5
(17.333, 25.0) 2
NaN 4
This approach is useful if you want to keep the order of the first Series but still include NaNs in the plot.
Conclusion
In this article, we explored how to plot bar graphs using pandas’ cut function and interval when dealing with NaNs. We discussed two approaches: one that uses dropna=False in value_counts and another that uses sort_index(na_position='last'). Both approaches allow us to reuse the bins created from the first column (vals1) to plot the value counts for both columns, including NaNs as a separate category/bin.
Last modified on 2024-03-27