Nov 25, 2020
353: Red for the Ones That Might Blow Up
Play • 1 hr 16 min

Seth Hillbrand (@SethHillbrand), lead developer for KiCAD (@kicad_pcb), spoke with us about open source development, EDA tools, pronunciation, and inclusion.

Check out KiCAD!

Seth’s company provides support for KiCAD (kipro-pcb.com, @kiproeda).


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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