# How to Fix a Blank Plot Issue in Pandas for Customer Order Comparison

If you’re scratching your head wondering why you’re getting a blank plot when attempting to compare customer order data in Pandas, you’re not alone. Let’s dive into the issue using the provided example dataset and explore a solution to rectify the blank plot problem.

## 1. Understanding the Scenario.

1. Consider a scenario where you have customer order data presented in the following format:
```    Customer No Customer Type                    Order Time  Order Amount  Order Quantity
0           126       Regular 2023-01-01 00:00:00.000000000    279.330970               6
1           126       Regular 2023-01-01 04:24:29.387755102     75.071612               4
2           126       Regular 2023-01-01 08:48:58.775510204    253.021647               2
3           126       Regular 2023-01-01 13:13:28.163265306     58.994449               9
4           126       Regular 2023-01-01 17:37:57.551020408    248.769915               1
5           126       Regular 2023-01-01 22:02:26.938775510    490.814028               5
6           126       Regular 2023-01-02 02:26:56.326530612    211.750009               7
7           126       Regular 2023-01-02 06:51:25.714285714    266.402089               6
8           126       Regular 2023-01-02 11:15:55.102040816    359.897532               8
9           126       Regular 2023-01-02 15:40:24.489795918    446.214150               9
10          186       Regular 2023-01-02 20:04:53.877551020    463.205960               9
11          186       Regular 2023-01-03 00:29:23.265306122    147.569962               3
12          186       Regular 2023-01-03 04:53:52.653061224    304.334990               9
13          186       Regular 2023-01-03 09:18:22.040816326    439.296153               7
14          186       Regular 2023-01-03 13:42:51.428571428    279.036032               7```
2. The objective is to compare the data for each ‘Customer No‘ against the most recent information within a specified time range.
3. The goal is to plot the ‘Order Quantity‘ against ‘Order Time‘ for each customer separately.

## 2. The Code and the Blank Plot Issue.

1. You’ve crafted a code snippet to achieve this objective, but upon execution, you notice that it returns a blank plot.
2. Let’s examine the original code:
```import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Generate random data for the example dataset
np.random.seed(0)

num_customers = 5
num_orders_per_customer = 10

customer_nos = np.random.choice(range(100, 200), size=num_customers, replace=False)
customer_types = np.random.choice(['Regular', 'Non Regular'], size=num_customers)
order_times = pd.date_range(start='2023-01-01', end='2023-01-10', periods=num_customers*num_orders_per_customer)
order_amounts = np.random.uniform(50, 500, size=num_customers*num_orders_per_customer)
order_quantities = np.random.randint(1, 10, size=num_customers*num_orders_per_customer)

# Create the DataFrame
data = {
'Customer No': np.repeat(customer_nos, num_orders_per_customer),
'Customer Type': np.repeat(customer_types, num_orders_per_customer),
'Order Time': order_times,
'Order Amount': order_amounts,
'Order Quantity': order_quantities
}

df = pd.DataFrame(data)
print("Original df: ")
print(df)
print("******************************************************")

# Define a plotting function
def plot_customer(x):
plt.plot(x['Order Time'], x['Order Amount'])#, marker='o')
plt.xlabel('Order Time')
plt.ylabel('Order Amount')
plt.title('Order Amount vs Order Time for Customer No: ' + str(x['Customer No'].iloc[0]))
plt.gcf().autofmt_xdate()
plt.ylim(0, x['Order Amount'].max() * 1.1)
plt.show()

# Group the DataFrame by 'Customer No'
grouped_df = df.groupby('Customer No')

# Iterate over groups and print each group
for name, group in grouped_df:
print(f"Customer No: {name}")
print(group)

print("******************************************************")

# Apply the plotting function to each group
grouped_df.apply(plot_customer)
```
3. The issue stems from the absence of markers in the plot. Without markers, the plot may not display any visible data points, resulting in a seemingly blank plot.

## 3. The Solution.

1. To rectify this, add the ‘marker‘ parameter to the `plt.plot` function within the plotting function:
`plt.plot(x['Order Time'], x['Order Amount'], marker='o') # Add marker parameter`
2. Now, with markers added, you should see your customer order data points plotted over time.
3. With these adjustments, the blank plot issue should be resolved, and your customer order data will be accurately visualized over time.
4. When you run the above code example, it will generate the below output in the console.
```Original df:
Customer No Customer Type                    Order Time  Order Amount  Order Quantity
0           126       Regular 2023-01-01 00:00:00.000000000    279.330970               6
1           126       Regular 2023-01-01 04:24:29.387755102     75.071612               4
2           126       Regular 2023-01-01 08:48:58.775510204    253.021647               2
3           126       Regular 2023-01-01 13:13:28.163265306     58.994449               9
4           126       Regular 2023-01-01 17:37:57.551020408    248.769915               1
5           126       Regular 2023-01-01 22:02:26.938775510    490.814028               5
6           126       Regular 2023-01-02 02:26:56.326530612    211.750009               7
7           126       Regular 2023-01-02 06:51:25.714285714    266.402089               6
8           126       Regular 2023-01-02 11:15:55.102040816    359.897532               8
9           126       Regular 2023-01-02 15:40:24.489795918    446.214150               9
10          186       Regular 2023-01-02 20:04:53.877551020    463.205960               9
11          186       Regular 2023-01-03 00:29:23.265306122    147.569962               3
12          186       Regular 2023-01-03 04:53:52.653061224    304.334990               9
13          186       Regular 2023-01-03 09:18:22.040816326    439.296153               7
14          186       Regular 2023-01-03 13:42:51.428571428    279.036032               7
15          186       Regular 2023-01-03 18:07:20.816326530    462.525329               2
16          186       Regular 2023-01-03 22:31:50.204081632    464.520925               7
17          186       Regular 2023-01-04 02:56:19.591836734     87.400622               9
18          186       Regular 2023-01-04 07:20:48.979591836    174.973353               9
19          186       Regular 2023-01-04 11:45:18.367346938     54.210517               4
20          102   Non Regular 2023-01-04 16:09:47.755102040    429.053936               3
21          102   Non Regular 2023-01-04 20:34:17.142857142    341.228363               4
22          102   Non Regular 2023-01-05 00:58:46.530612244    428.623754               7
23          102   Non Regular 2023-01-05 05:23:15.918367346    169.128574               4
24          102   Non Regular 2023-01-05 09:47:45.306122449    229.019339               7
25          102   Non Regular 2023-01-05 14:12:14.693877551    298.769666               6
26          102   Non Regular 2023-01-05 18:36:44.081632653    124.223207               8
27          102   Non Regular 2023-01-05 23:01:13.469387755    216.413642               1
28          102   Non Regular 2023-01-06 03:25:42.857142857    115.898793               9
29          102   Non Regular 2023-01-06 07:50:12.244897959    306.328283               5
30          155   Non Regular 2023-01-06 12:14:41.632653061    366.681776               7
31          155   Non Regular 2023-01-06 16:39:11.020408163    179.814397               6
32          155   Non Regular 2023-01-06 21:03:40.408163265    244.979628               9
33          155   Non Regular 2023-01-07 01:28:09.795918367    390.248012               3
34          155   Non Regular 2023-01-07 05:52:39.183673469    228.244224               4
35          155   Non Regular 2023-01-07 10:17:08.571428571    453.217274               8
36          155   Non Regular 2023-01-07 14:41:37.959183673    337.514484               6
37          155   Non Regular 2023-01-07 19:06:07.346938775    451.199497               4
38          155   Non Regular 2023-01-07 23:30:36.734693877    356.025006               5
39          155   Non Regular 2023-01-08 03:55:06.122448979    252.138982               6
40          175       Regular 2023-01-08 08:19:35.510204081    490.356918               4
41          175       Regular 2023-01-08 12:44:04.897959183    102.290859               4
42          175       Regular 2023-01-08 17:08:34.285714285    395.160667               8
43          175       Regular 2023-01-08 21:33:03.673469387    235.319063               8
44          175       Regular 2023-01-09 01:57:33.061224489    353.947587               4
45          175       Regular 2023-01-09 06:22:02.448979591    162.408324               3
46          175       Regular 2023-01-09 10:46:31.836734693    190.948249               4
47          175       Regular 2023-01-09 15:11:01.224489795    484.437299               8
48          175       Regular 2023-01-09 19:35:30.612244898    314.809288               8
49          175       Regular 2023-01-10 00:00:00.000000000    346.850785               6
******************************************************
Customer No: 102
Customer No Customer Type                    Order Time  Order Amount  Order Quantity
20          102   Non Regular 2023-01-04 16:09:47.755102040    429.053936               3
21          102   Non Regular 2023-01-04 20:34:17.142857142    341.228363               4
22          102   Non Regular 2023-01-05 00:58:46.530612244    428.623754               7
23          102   Non Regular 2023-01-05 05:23:15.918367346    169.128574               4
24          102   Non Regular 2023-01-05 09:47:45.306122449    229.019339               7
25          102   Non Regular 2023-01-05 14:12:14.693877551    298.769666               6
26          102   Non Regular 2023-01-05 18:36:44.081632653    124.223207               8
27          102   Non Regular 2023-01-05 23:01:13.469387755    216.413642               1
28          102   Non Regular 2023-01-06 03:25:42.857142857    115.898793               9
29          102   Non Regular 2023-01-06 07:50:12.244897959    306.328283               5

Customer No: 126
Customer No Customer Type                    Order Time  Order Amount  Order Quantity
0          126       Regular 2023-01-01 00:00:00.000000000    279.330970               6
1          126       Regular 2023-01-01 04:24:29.387755102     75.071612               4
2          126       Regular 2023-01-01 08:48:58.775510204    253.021647               2
3          126       Regular 2023-01-01 13:13:28.163265306     58.994449               9
4          126       Regular 2023-01-01 17:37:57.551020408    248.769915               1
5          126       Regular 2023-01-01 22:02:26.938775510    490.814028               5
6          126       Regular 2023-01-02 02:26:56.326530612    211.750009               7
7          126       Regular 2023-01-02 06:51:25.714285714    266.402089               6
8          126       Regular 2023-01-02 11:15:55.102040816    359.897532               8
9          126       Regular 2023-01-02 15:40:24.489795918    446.214150               9

Customer No: 155
Customer No Customer Type                    Order Time  Order Amount  Order Quantity
30          155   Non Regular 2023-01-06 12:14:41.632653061    366.681776               7
31          155   Non Regular 2023-01-06 16:39:11.020408163    179.814397               6
32          155   Non Regular 2023-01-06 21:03:40.408163265    244.979628               9
33          155   Non Regular 2023-01-07 01:28:09.795918367    390.248012               3
34          155   Non Regular 2023-01-07 05:52:39.183673469    228.244224               4
35          155   Non Regular 2023-01-07 10:17:08.571428571    453.217274               8
36          155   Non Regular 2023-01-07 14:41:37.959183673    337.514484               6
37          155   Non Regular 2023-01-07 19:06:07.346938775    451.199497               4
38          155   Non Regular 2023-01-07 23:30:36.734693877    356.025006               5
39          155   Non Regular 2023-01-08 03:55:06.122448979    252.138982               6

Customer No: 175
Customer No Customer Type                    Order Time  Order Amount  Order Quantity
40          175       Regular 2023-01-08 08:19:35.510204081    490.356918               4
41          175       Regular 2023-01-08 12:44:04.897959183    102.290859               4
42          175       Regular 2023-01-08 17:08:34.285714285    395.160667               8
43          175       Regular 2023-01-08 21:33:03.673469387    235.319063               8
44          175       Regular 2023-01-09 01:57:33.061224489    353.947587               4
45          175       Regular 2023-01-09 06:22:02.448979591    162.408324               3
46          175       Regular 2023-01-09 10:46:31.836734693    190.948249               4
47          175       Regular 2023-01-09 15:11:01.224489795    484.437299               8
48          175       Regular 2023-01-09 19:35:30.612244898    314.809288               8
49          175       Regular 2023-01-10 00:00:00.000000000    346.850785               6

Customer No: 186
Customer No Customer Type                    Order Time  Order Amount  Order Quantity
10          186       Regular 2023-01-02 20:04:53.877551020    463.205960               9
11          186       Regular 2023-01-03 00:29:23.265306122    147.569962               3
12          186       Regular 2023-01-03 04:53:52.653061224    304.334990               9
13          186       Regular 2023-01-03 09:18:22.040816326    439.296153               7
14          186       Regular 2023-01-03 13:42:51.428571428    279.036032               7
15          186       Regular 2023-01-03 18:07:20.816326530    462.525329               2
16          186       Regular 2023-01-03 22:31:50.204081632    464.520925               7
17          186       Regular 2023-01-04 02:56:19.591836734     87.400622               9
18          186       Regular 2023-01-04 07:20:48.979591836    174.973353               9
19          186       Regular 2023-01-04 11:45:18.367346938     54.210517               4

******************************************************```

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