24. What will be the output of the following code?
x = [1, 2, 3, 4, 5]
x[2:4] = [10, 20] print(x)
x = [1, 2, 3, 4, 5]
x[2:4] = [10, 20] print(x)
Anonymous Quiz
32%
a) [1, 2, 10, 20, 5]
29%
b) [1, 2, 10, 20, 4, 5]
15%
c) [10, 20, 3, 4, 5]
24%
d) Error
25. What will be the result of the following code?
x = "123"
print(int(x) + 2)
x = "123"
print(int(x) + 2)
Anonymous Quiz
53%
a) 125
23%
b) 1232
5%
c) 124
20%
d) Error
π2
SERIAL POSITION EFFECT
We're conducting a fun and simple experiment to explore how people remember words!
What you'll do:
You'll see a list of 20 words for 20 seconds, α³αα αα α¨20 α°α¨αα΅ α αα α α΅α αα
After that, youβll recall and type as many words as you can remember, αααα ααα΅ αααΈαα°α΅ ααα©
Itβs totally voluntary, and your participation will help us understand how memory works better!
Why participate?
Challenge your memory skills
Contribute to an interesting research experiment
Click here to start! β‘οΈ Google Form
Letβs see how well you can remember the words!
We're conducting a fun and simple experiment to explore how people remember words!
What you'll do:
You'll see a list of 20 words for 20 seconds, α³αα αα α¨20 α°α¨αα΅ α αα α α΅α αα
After that, youβll recall and type as many words as you can remember, αααα ααα΅ αααΈαα°α΅ ααα©
Itβs totally voluntary, and your participation will help us understand how memory works better!
Why participate?
Challenge your memory skills
Contribute to an interesting research experiment
Click here to start! β‘οΈ Google Form
Letβs see how well you can remember the words!
Google Docs
Serial Position Effect Experiment
Welcome to the Serial Position Effect Experiment!"
In this experiment, we aim to prove the validity of what weβve learned about the serial position effect. The idea that people tend to remember words at the beginning (primacy effect) and the end (recencyβ¦
In this experiment, we aim to prove the validity of what weβve learned about the serial position effect. The idea that people tend to remember words at the beginning (primacy effect) and the end (recencyβ¦
π1
SERIAL POSITION EFFECT
Just completed the serial position effect experiment! The results didnβt show a perfect U-shape, but it closely resembled one.
With only 11 participants, the pattern was still visible although more participants might have made it clearer. Itβs amazing how memory shows such predictable trends. The primacy and recency effects are also clearly visible.
The graph is made using the Python module Matplotlib. Check it out above.
#SerialPositionEffect
@python_pioneers
Just completed the serial position effect experiment! The results didnβt show a perfect U-shape, but it closely resembled one.
With only 11 participants, the pattern was still visible although more participants might have made it clearer. Itβs amazing how memory shows such predictable trends. The primacy and recency effects are also clearly visible.
The graph is made using the Python module Matplotlib. Check it out above.
#SerialPositionEffect
@python_pioneers
π2
Your skills are like a supercar.......don't let them rust in the wrong environment. Seek challenges that push you to your limits.
#Quote
#PushYourLimits
@python_pioneers
π2
π 4000 Strong! π
We just hit 4,000 subscribers! π Your support, engagement, and love keep this community growing every day. Thank you for being part of this journey!
Let's keep learning, sharing, and inspiring together. More exciting content is on the way!
#RoadTo5K
#ThankYou
#CommunityStrong
@python_pioneers
We just hit 4,000 subscribers! π Your support, engagement, and love keep this community growing every day. Thank you for being part of this journey!
Let's keep learning, sharing, and inspiring together. More exciting content is on the way!
#RoadTo5K
#ThankYou
#CommunityStrong
@python_pioneers
π₯4β€1π1
Next: Data Visualization in Python
α αα£α α αα΅α αα¨α Pythonα α αα αα α₯αα΄α΅ visualize αα¨α α₯αα°ααα½α α₯αα«ααα’
Stay Tuned!
#DataVisualizationInPython
@python_pioneers
α αα£α α αα΅α αα¨α Pythonα α αα αα α₯αα΄α΅ visualize αα¨α α₯αα°ααα½α α₯αα«ααα’
Stay Tuned!
#DataVisualizationInPython
@python_pioneers
π Understanding Data Visualization in Python
πΉData Visualization ααα΅αα?
Data visualization α¨αααα α αα΅α αα¨α graphically ααα α αα³α ααα© α¨ααα΅ααα₯α α΅ ααα represent α¨ααα¨αα α΅ αα°α΅ ααα’
α Data Processing stages αα΅α₯ Data visualizationα α¨ααα£α α Data Output ααα Data interpretation α₯α Analysis α°α¨α αα ααα’
Data Output stage αα α₯αα° α₯α charts, graphs, α¨αα³α°αα΅ α¨visualization tools processed αα¨αα ααα¨α³α΅ α ααα ααα© α₯αα΅αα΅ααα₯ αα¨α±ααα’
Data Interpretation α₯α Analysis stage αα visualization α¨ α αα΅α αα¨α pattern ααα trend α₯αα΅αα¨α³α‘ α₯αα αα¨α αα α¨α°αα α¨α° αα³α α₯αα΅ααα΅α α«ααααα’
To be cont'd
#DataVisualization
#Data
@python_pioneers
πΉData Visualization ααα΅αα?
Data visualization α¨αααα α αα΅α αα¨α graphically ααα α αα³α ααα© α¨ααα΅ααα₯α α΅ ααα represent α¨ααα¨αα α΅ αα°α΅ ααα’
α Data Processing stages αα΅α₯ Data visualizationα α¨ααα£α α Data Output ααα Data interpretation α₯α Analysis α°α¨α αα ααα’
Data Output stage αα α₯αα° α₯α charts, graphs, α¨αα³α°αα΅ α¨visualization tools processed αα¨αα ααα¨α³α΅ α ααα ααα© α₯αα΅αα΅ααα₯ αα¨α±ααα’
Data Interpretation α₯α Analysis stage αα visualization α¨ α αα΅α αα¨α pattern ααα trend α₯αα΅αα¨α³α‘ α₯αα αα¨α αα α¨α°αα α¨α° αα³α α₯αα΅ααα΅α α«ααααα’
Good data visualization enhances understanding and communication, making complex data more accessible.
To be cont'd
#DataVisualization
#Data
@python_pioneers
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α Python, data visualization moduleα¦α½ α α₯ααα
ααα α°α¨ααα½ αα°α«αα’
1. Import the required library β Common libraries include matplotlib, seaborn, and plotly.
2. Prepare the data β Load or create a dataset.
3. Create the plot β Use functions to generate a visualization.
4. Customize the visualization β Add labels, titles, colors, or styles.
5. Display the visualization β Render the plot.
This process applies to various chart types like bar plots, histograms, and scatter plots, depending on the library and data used.
to be cont'd
#DataVisualization
#Data
@python_pioneers
1. Import the required library β Common libraries include matplotlib, seaborn, and plotly.
import matplotlib.pyplot as plt
import seaborn as sns
2. Prepare the data β Load or create a dataset.
data = [10, 20, 15, 25, 30]
3. Create the plot β Use functions to generate a visualization.
plt.plot(data)
4. Customize the visualization β Add labels, titles, colors, or styles.
plt.title("Simple Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")5. Display the visualization β Render the plot.
plt.show()
This process applies to various chart types like bar plots, histograms, and scatter plots, depending on the library and data used.
to be cont'd
#DataVisualization
#Data
@python_pioneers
π3β‘1
π1