<jupyter_start><jupyter_text>In this class we will use Jupyter LAb Installation instructions: https://www.youtube.com/watch?v=uOwCiZKj2rg Install Anaconda Navigator and Jupyter Lab  great python numpy tutorials  in Jupyter lab: https://www.youtube.com/watch?v=7wfPqAyYADY&list=PLcD0MjpSMpGRoZgeb509_yICnabm4PYqc markdown link[link to Jupyter Lab Tutorials](https://www.youtube.com/watch?v=7wfPqAyYADY&list=PLcD0MjpSMpGRoZgeb509_yICnabm4PYqc)  Hebrew tutorial for Jupyter Notebook (the old version before Jupyter Lab) https://www.youtube.com/watch?v=g9EglM7Ymns       data sciene income:. https://www.sqlink.com/salary/ https://www.nisha.co.il/%D7%98%D7%91%D7%9C%D7%90%D7%95%D7%AA-%D7%A9%D7%9B%D7%A8 https://www.alljobs.co.il/User/SalaryCompound/position/?JobCategoryID=1733  same Python commands workprint("Hello Jupyter Lab")  In Jupyter Lab there are modes: Command mode and Edit mode A Script can be run as a Python Code or as a Markdown Markdown in wikipedia (https://en.wikipedia.org/wiki/Markdown)  Jupiter Lab keyboard shortcuts https://gist.github.com/discdiver/9e00618756d120a8c9fa344ac1c375ac keyboard shotrctus greatly increase productivity, here are some commons: note there are modes: Command mode (outside the cells) and edit mode (inside a cell) shortcut to edit mode -> command mode: Esc shortcut to command mode ->  edit mode: Enter Ctrl+B toggle hide/show left sidebar Ctrl + / toggle comment Ctrl + ] indent  Dedent: Ctrl + [<jupyter_code># jupyter lab has a markdown \ code option mode.
# (Header 1, title)
## (Header 2, major headings)
### (Header 3, subheadings)
#### (Header 4)
##### (Header 5)
###### (Header 6)``
# cmd view point denoted by ! sign
!pip install numpy
!pip install pandas

# read file using pandas
# pandas is a fast, powerful, flexible and easy to use open source data analysis in python
import pandas as pd
data = pd.read_csv('titanic_short.csv')
data.head() # we can set the number of first rows to be displayed.
# guess what is the commmand for the end
# same goes for tail

data.tail(2)
#### Set specific cells:
data[10:19]
# self excercise: suggest how to display only survivors (denoted in 1)

data[data['survived'] == 1]
# self excercise: suggest how to display only females gender
data[data['sex']=='female']
# self excercise: suggest how to display only females survivors
data[(data['survived'] == 1) & (data['sex']=='female')]
#### display some of the collumns by a list of collumns
data[ [ 'pclass','age'] ] 
#### Number of rows and collumns by hsape attribute:
data.shape
#### Get more information such as the column names by info() method
data.info()
#### the type of the read data is data frame
type(data)<jupyter_output><empty_output><jupyter_text>set nice equations Sphere or Quadratic equations::$ x^2 + y^2 = z^2 \\$$ \\ x_1,_2 =  \frac{- b\pm \sqrt{b^2-4ac}}{2a} $ Emphasis, aka italics, with *asterisks* or _underscores_.Strong emphasis, aka bold, with **asterisks** or __underscores__.Combined emphasis with **asterisks and _underscores_**.Strikethrough uses two tildes. ~~Scratch this.~~<jupyter_code>import numpy as np
arr = np.array([1, 2, 3, 4, 5])
# . product
print(arr*arr)<jupyter_output>[ 1  4  9 16 25]<jupyter_text>vector multiplicationarr.dot(arr) or by <EMAIL><jupyter_code># Get current directory

import os
os.getcwd()
#### Reset kernel by: Menu -> Kernel -> Restart Kenel<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '577946', 'license': 'MIT', 'n_tokens_mistral': 1232, 'n_tokens_neox': 1135, 'n_words': 378, 'path': 'shai_yona_tirgol/1/tirgul1.ipynb', 'repo_name': 'Liron-Firuz/Data-analsis-lesson-2021A', 'size': '3378'}