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