Nov 12, 2020
351: Dextral or Sinistral
Play • 57 min

Chris and Elecia discuss transcripts, lightsabers, seashells, python, numpy, matlab and how to get into embedded systems development.

Embedded show transcripts are available at embedded.fm/transcripts 

Elecia’s origami github repository includes a python script for generating origami shell folding patterns. The paper described was Analysis of Shell Coiling: General Problems by David M. Raup from the Journal of Paleontology , Sep., 1966, Vol. 40, No. 5.

Chris used this model to print his lightsaber: Star Wars Lightsaber (Normal version) from YouMagine

The episode was sponsored by Triplebyte. If you are looking to prove your skills, develop your knowledge, or find a job you love, check out Triplebyte.

Hacker Public Radio
Hacker Public Radio
Hacker Public Radio
HPR3253: Pandas Intro
Welcome to another episode of HPR I'm your host Enigma and today we are going to be talking about one of my favorite python modules Pandas This will be the first episode in a series I'm naming: For The Love of Python. First we need to get the module pip or pip3 install pandas This will install numpy as well Pandas uses an object called a dataframe which is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Think of a spreadsheet type object in memory Today we are going to talk about: *1) Importing data from various sources* Csv, excel, sql. More advance topics like Json covered in another episode. df = pd.read_csv('file name') *2) Accessing data by column names or positionally * print(df.head(5)) # print all columns only first 5 rows print(df.tail(5)) # print all columns only last 5 rows print(df.shape) # print number of rows and columns in dataframe print(df.columns) print column names print(df[0:1].head(5)) print first two columns first 5 values by column position print(df['field1].head(5)) print same column first five values by column name *3) Setting column types. * df['FieldName'] = df['FieldName'].astype(int) # sets column as interger df['FieldName'] = df['FieldName'].astype(str) # sets column to string df['DateColumn'] = pd.to_datetime(df['DateColumn']) # sets column to Datetime *4) Some basic filtering/manipulation of data. * Splits string at the @ for one split next two lines create 2 columns that use the pieces. new = df2["Email"].str.split("@", n = 1, expand = True) df2["user"]= new[0] df2["domain"]= new[1] df['col'] = df['Office'].str[:3] # creates a new column grabing the first 3 positions of Office column df = df[df['FieldName'] != 0] # Only keep rows that have a FieldName value not equal to zero See example code that you can run at: Pandas Working example
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