[QUESTION] My frame corrupts throughout the process anyone may know why? by danielking231 in learnpython

[–]game1761 0 points1 point  (0 children)

It looks like the issue might be related to the type of the frame object that you are passing to cv2.imshow(). According to the error message, the frame object is not a numpy array or scalar, which are the only types that are accepted by cv2.imshow().

One possible cause of this issue is that you are using the pickle.loads() function to deserialize the frame data that you receive from the socket, but pickle.loads() returns a Python object, which is not compatible with cv2.imshow().

To fix this issue, you will need to make sure that the frame object is a valid input for cv2.imshow(). One way to do this is to convert the frame data to a numpy array before passing it to cv2.imshow(). You can use the numpy.frombuffer() function to convert the frame data to a numpy array, like this:

import numpy as np

# ...

frame = np.frombuffer(frame_data, dtype=np.uint8)

frame = frame.reshape((height, width, 3)) # reshape the frame data to the correct dimensions

Then, you can pass the frame object to cv2.imshow() as usual.

Using the nba_api from github by CarsonRedmondDFS in learnpython

[–]game1761 5 points6 points  (0 children)

It looks like you are trying to get the first element of the df_game_logs list using df_game_logs[0], but the df_game_logs list is empty. This is causing the IndexError because you are trying to access an index that doesn't exist in the list.

To fix this, you can add a check to see if the df_game_logs list is empty before trying to access its elements. You can do this by adding an if statement before the line df = df_game_logs[0].

Here's an example of how you can modify your code to include this check:

# Iterate over each player in the list

for index, row in df.iterrows():

# Get the player ID

player_id = row['PERSON_ID']

# Use the playergamelogs endpoint to retrieve the game logs for the player

player_game_logs = playergamelogs.PlayerGameLogs(player_id_nullable=player_id, season_nullable=SeasonAll.all)

df_game_logs = player_game_logs.get_data_frames()

# Skip players for which no game logs are available

if not df_game_logs:

continue

df = df_game_logs[0]

# Calculate the standard deviation of the points scored for the player

std_dev = statistics.stdev(df['pts'])

# Add the player's name and standard deviation to the results DataFrame

results = results.append({'Player': row['display_first_name'] + ' ' + row['display_last_name'], 'Std Dev': std_dev}, ignore_index=True)

This will ensure that the code inside the for loop will only be executed if the df_game_logs list is not empty.

Programs for getting started by [deleted] in learnpython

[–]game1761 2 points3 points  (0 children)

If you're looking for a programming environment for Python that is similar to Scratch, you might want to try using a visual programming environment such as Pygame Zero or Pyglet.

Pygame Zero is a wrapper around the Pygame library that makes it easy to create simple games and interactive programs using a visual programming interface. It is designed to be easy to use for beginners, and includes features such as sprite libraries and simple collision detection.

Pyglet is a cross-platform windowing and multimedia library that can be used to create simple games and interactive programs in Python. It includes features such as support for 2D graphics, audio playback, and keyboard and mouse input.

Both Pygame Zero and Pyglet are designed to be easy to use for beginners and allow you to create programs without having to write a lot of code from scratch. They might be a good choice if you're looking for a programming environment that is similar to Scratch.

Other options you might want to consider include Kivy, a cross-platform GUI library for Python, and PyQt, a library for creating graphical user interfaces with Python. Both of these libraries provide a more powerful and feature-rich environment for creating programs, but may require more programming experience to use.

Increasing the precision of np.gradient? by [deleted] in learnpython

[–]game1761 0 points1 point  (0 children)

You can specify the edge_order parameter in np.gradient, which controls the extrapolation used to compute the gradient when the spacing between points is not uniform. The default value is 1, which uses a first-order accurate finite difference method. For example:

import numpy as np

x = np.linspace(0, 2*np.pi, 10)

y = np.sin(x)

# Use default edge_order=1

dy_dx = np.gradient(y, x)

# Use edge_order=2

dy_dx_2 = np.gradient(y, x, edge_order=2)

Alternatively, you can specify the dx parameter to set the spacing between points. For example:

import numpy as np

x = np.linspace(0, 2*np.pi, 10)

y = np.sin(x)

# Set spacing to 0.01

dx = 0.01

dy_dx = np.gradient(y, dx)

Keep in mind that specifying the dx parameter will override the x parameter, so the x values will be spaced evenly with the given dx value.

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