Session 14 - Advanced Numpy | Data Science Mentorship Program (DSMP) 2022-23 | Free Session

[@nikunjjain5212]

Every topic is easy...if aap pdha rhe hai . Broadcasting is Crystal clear 😉. Thank you 😊

[@123arskas]

You're a Monster, a Beast. I can't even memorize the stuff that you explain even though I understood it all and practiced it all. Meanwhile you just know everything. You're awesome.

[@abdulhannan-go8hl]

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[@karankeshav1]

It's not that much difficult, although he has a teaching experience as well as working experience...so for a person who has both type of experience , it's not that much difficult to teach something. But also he is working hard and teaching and managing all the things itself

[@priyanshuprajapati539]

20:20 It won't throw an error because `np.arange` clips (or wraps around) values that fall outside the representable range of `np.int8`. Here's a breakdown of what happens: 1. **`np.arange(10000000, dtype=np.int8)`:** This line attempts to create a NumPy array named `a` with elements ranging from 0 (inclusive) to 9999999 (exclusive) using the `int8` data type. 2. **`int8` Range:** As you know, `int8` can only store values from -128 to 127. 3. **Clipping:** Since the requested starting and ending points (0 and 9999999) are within the representable range of `int8`, `np.arange` doesn't raise an error. However, any numbers generated beyond the valid range are "clipped" to fit within the limits. **In essence, the array `a` will actually contain:** * The first 127 elements will be values from 0 to 127 (as intended). * The remaining elements (up to 9999999) will be "wrapped around" and start over from -128. This repetition continues until the desired length (10000000) is reached. **`sys.getsizeof(a)`:** This line calls the `sys.getsizeof` function to determine the size of the array `a` in bytes. This size will reflect the actual memory allocated for the array, which depends on the number of elements and the data type used. While no error is thrown, this behavior might not be what you intended. If you need to store numbers within the range 0 to 9999999, you should consider using a larger data type like `np.int16` or `np.int32` that can accommodate these values.

[@priyanshuprajapati539]

Clipped off and wrapped around, in the context of NumPy arrays and data types, refer to how values outside the representable range of a data type are handled. Here's a breakdown of each term: **Clipped Off:** * When a value attempts to exceed the maximum positive value a data type can store, it gets "clipped" to the maximum. * Similarly, if a value tries to fall below the minimum negative value, it gets "clipped" to the minimum. Imagine a data type with a range of 0 to 100. Any number greater than 100 would be "clipped" down to 100, and any number less than 0 would be "clipped" up to 0. **Wrapped Around:** * This concept applies specifically to signed integer data types, like `int8` in your example. * If a value attempts to exceed the maximum positive value (e.g., 127 for `int8`), it doesn't raise an error. Instead, it "wraps around" and starts over from the minimum negative value (-128 for `int8`). This continues in a cyclical pattern. Think of it like a circular number line. Reaching the end (maximum) makes you jump back to the beginning (minimum) and continue counting. **Example:** ```python import numpy as np # Array with values outside int8 range arr = np.array([150, -130, 5], dtype=np.int8) print(arr) # Output: [-6, 127, 5] # 150 is clipped to the maximum (127) # -130 is wrapped around to the minimum (127) and then clipped to -128 # 5 remains within the range ``` Here, 150 is clipped to 127, the maximum representable value for `int8`. -130 is wrapped around to -128 (minimum) but since -128 is also the minimum limit, it stays there. Finally, 5 remains unchanged as it falls within the valid range. **Key Points:** * Clipping off generally occurs with both signed and unsigned integer data types. * Wrapping around is specific to signed integer data types where values can cycle through the range. * It's important to choose a data type that can accommodate the range of values you intend to store in your NumPy arrays. I hope this explanation clarifies the concepts of clipping off and wrapping around!

[@pradhumn5503]

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[@rohinisingh6916]

You are the great sir . awesome explanation ☺️.

[@sajjaduddin8188]

Thank you once again

[@mohitdhillon3153]

Thanks!

[@makarsh29]

best teacher ever

[@tanvirhasanmahmud]

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[@anilrathore.bigthink]

really increadible! ultimate pro max teacher

[@descendantsoftheheroes_660]

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[@somanshkumar1325]

Super Amazing session. Very much informative, thoroughly enjoyed the session. Thank you so much, Nitish :)

[@raosajid6578]

great explanation sir

[@lokeshagarwal6373]

very very Nice Sir

[@SameerAli-nm8xn]

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[@DeepakPandey-dv8cn]

thank so much sir

[@siddhigolatkar8558]

Thank you Sir

[@Shaikh-Ji]

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[@DeepakPandey-dv8cn]

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[@mehediazad1780]

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[@yasirbhatti7607]

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[@prashantmaurya9635]

IDK why, but when you said "haan, main missing value hu" is too funny for me. 🤣🤣🤣 BTW a great explanation.

[@shwetan1250]

Sir want to join this mentorship program due to got this on Christmas holiday. But i think i m late to join now what to do .😔😔🤨🤨

[@sujithsaikalakonda4863]

Wonderful session sir.

[@subenakhatun-z2r]

I want to join this course but do not have a credit card. I am from Bangladesh. please Sir make any system that's why outside India anyone can join.

[@jasmeetkaur-zy9mq]

but sir , this rules cannot be applied upon all the examples, like: (3,2) (3)

[@balrajprajesh6473]

Best

[@umangmittal750]

Can anyone provide me the solution of the task given in each class session? Please

[@AmritSwarn-x5u]

Bhai phir ye int8 1 crore ko kaise store kar lega at 17:59 if it can only store up to 256?

[@ShivamKumar-sp2dh]

i want to join the paid membership for whole data science course

[@ShivamKumar-sp2dh]

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data science course fee???

[@yatinshekhar787]

29/140

[@Who_care360]

love you sir