> For the complete documentation index, see [llms.txt](https://laboratory-of-lipid-metabolism-a.gitbook.io/omics-data-visualization-in-r-and-python/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://laboratory-of-lipid-metabolism-a.gitbook.io/omics-data-visualization-in-r-and-python/missing-values-handling-in-python/detecting-missing-values.md).
# Detecting missing values
## Required packages
The required packages for this section are pandas, matplotlib and seaborn. These can be installed with the following command in the command window (Windows) / terminal (Mac).
```
pip install pandas matplotlib seaborn
```
## Loading the data
{% file src="/files/1f5syZV6urkUaZVGexSc" %}
Place the downloaded Lipidomics\_missing\_values\_EXAMPLE.xlsx file in the same folder as your JupyterLab script. Then run the following code in Jupyter:
```python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_excel("Matrix_missing_values_EXAMPLE.xlsx", decimal=",")
df.set_index("Sample Name", inplace=True)
```
We can generate a heatmap visualisation of the missing values across the table (white values indicate a missing value):
```python
plt.figure(figsize=(24, 30)) # Modify the width and height as needed
sns.heatmap(df.isnull(), cbar=False)
plt.savefig("missing_values_heatmap.png", dpi=200, bbox_inches='tight')
plt.show()
```
Heatmap indicating missing values in the data table, white values indicate missing data.
We can visualize the % missing values in the samples:
```python
# Calculate percentage of missing values per row (sample)
missing_percentage_per_sample = df.isnull().mean(axis=1) * 100
# Bar plot for missing values per sample
plt.figure(figsize=(30, 6))
missing_percentage_per_sample.sort_values(ascending=False).plot(kind='bar')
plt.title("Percentage of Missing Values per sample")
plt.xlabel("Samples")
plt.ylabel("Percentage Missing")
plt.tight_layout()
plt.show()
```
And for the species:
```python
# Calculate percentage of missing values per row (sample)
missing_percentage_per_sample = df.isnull().mean(axis=1) * 100
# Bar plot for missing values per sample
plt.figure(figsize=(30, 6))
missing_percentage_per_sample.sort_values(ascending=False).plot(kind='bar')
plt.title("Percentage of Missing Values per sample")
plt.xlabel("Samples")
plt.ylabel("Percentage Missing")
plt.tight_layout()
plt.show()
```
