Plotting Pandas DF with Numpy Arrays

plt.plot needs a list of x-coordinates together with an equally long list of y-coordinates. As you seem to want to use the index of the dataframe for the x-coordinate and each cell contents for the y-coordinates, you need to repeat the x-values as many times as the length of the y-coordinates.

Note that this format doesn't suit a line plot, as connecting subsequent points would create some strange vertical lines. plt.plot accepts a marker as its third parameter, for example '.' to draw a simple dot at each position.

A code example:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

N = 30
df = pd.DataFrame({f'column{c}':
                       [np.random.normal(np.random.uniform(10, 100), 1, np.random.randint(3, 11)) for _ in range(N)]
                   for c in range(1, 6)})
legend_handles = []
colors = plt.cm.Set1.colors
desired_columns = df.columns
for column, color in zip(desired_columns, colors):
    for ind, cell in df[column].iteritems():
        if len(cell) > 0:
            plotted, = plt.plot([ind] * len(cell), cell, '.', color=color)
    legend_handles.append(plotted)
plt.legend(legend_handles, desired_columns)
plt.show()

Note that pandas really isn't meant to store complete arrays inside cells. The preferred way is to create a dataframe in "long" form, with each value in a separate row (with the "index" repeated). Most functions of pandas and seaborn don't understand about arrays inside cells.

Here's a way to create a long form which can be called using Seaborn:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns

N = 30
df = pd.DataFrame({f'column{c}':
                       [np.random.normal(np.random.uniform(10, 100), 1, np.random.randint(3, 11)) for _ in range(N)]
                   for c in range(1, 6)})

desired_columns = df.columns
df_long_data = []
for column in desired_columns:
    for ind, cell in df[column].iteritems():
        for val in cell:
            dict = {'timestamp': ind, 'column_name': column, 'value': val}
            df_long_data.append(dict)
df_long = pd.DataFrame(df_long_data)
sns.scatterplot(x='timestamp', y='value', hue='column_name', data=df_long)
plt.show()

As per your problem, you have numpy arrays in each cell which you wanna plot. To pass your data to plt.plot() method you might need to pass every cell individually as whenever you try to pass it as a whole like you did, it is actually a sequence that you are passing. But the plot() method will accept a numpy array. This might help:

for column in df.columns:
    for cell in df[column]:
        plt.plot(cell)
        plt.show()