Write a Python script that loads a pipe seperated file into a pandas dataframe, prints out the basic information about the various columns, then graphs the distribution of values for each numeric column into subplots as part of a single image.
Here's a Python script that should do what you're looking for:

```python
import pandas as pd
import matplotlib.pyplot as plt

# Load the data into a pandas dataframe
df = pd.read_csv('filename.txt', sep='|')

# Print basic information about the dataframe
print(Dataframe Shape: , df.shape)
print(Columns: , df.columns)
print(Data Types: )
print(df.dtypes)

# Create subplots for each numeric column and plot the distribution of values
numeric_cols = df.select_dtypes(include=['int64', 'float64']).columns.tolist()
num_plots = len(numeric_cols)
fig, axs = plt.subplots(nrows=1, ncols=num_plots, figsize=(20, 5))
for i, col in enumerate(numeric_cols):
    axs[i].hist(df[col], bins=20)
    axs[i].set_title(col)
    axs[i].set_xlabel(Value)
    axs[i].set_ylabel(Frequency)
plt.show()
```


Here's how the script works:

1. The script imports the necessary libraries: pandas for data handling and matplotlib for plotting.
2. The script loads the data from the pipe-separated file into a pandas dataframe using the pd.read\_csv\(\) function.
3. The script prints out basic information about the dataframe using the `doc.body.textContent`, `doc.body.textContent`, and `doc.body.textContent` attributes.
4. The script selects the numeric columns in the dataframe using the select\_dtypes\(\) method and stores them in a list.
5. The script creates subplots for each numeric column using the subplots\(\) function and sets the number of rows and columns based on the number of numeric columns. It also sets the figure size to 20x5.
6. The script loops through each numeric column, plots the histogram of values using the hist\(\) method, sets the title, x-label, and y-label for each subplot.
7. Finally, the script displays the subplots using the show\(\) function.


Note: Replace the filename.txt with the actual file name with its path that you want to load. Also, make sure that the file is in the correct format and that the separator used in the file is indeed a pipe '|'.
The provided script functions properly. There was no error and everything worked straight out of the box.
However, your explanation of what the script is doing is not entirely correct. Actually all points are correct except point 3. 
Point 3 is completely unrelated to the script you have provided. Therefore, it should be removed from your reply.
Sorry, you're right, my explanation does seem to be incorrect.
However, it's not *entirely* incorrect. The script does print out basic information, it's just not using the `doc.body.textContent` attribute but rather the `df.shape`, `df.columns` and `df.dtypes` attributes.