Shantanu's Blog

Corporate Consultant

September 16, 2017

 

serialize python dataframe

Save all dataframes to a file in parquet format.
We can later simply open the files and quickly load all data into dataframe.

# serialize dataframe to a file:
import pandas as pd
from fastparquet import write, ParquetFile
!mkdir myparq1
for i in dir():
    if i.startswith('_'):
        pass
    else:
        if isinstance(locals()[i], pd.DataFrame):
            print  (i)
            write('myparq1/'+i+'.parq', locals()[i])

# open parquet file into dataframe:
for i in glob.glob('myparq1/*.parq'):
    x = os.path.basename(i).split('.')[0]
    print (i, " >> ", x)
    locals()[x] = ParquetFile(i).to_pandas()

Labels: , ,


August 28, 2017

 

Invoke Amazon lambda function directly

In most of the cases I access lambda function through API gateway. But there are times when I need to run the lambda function directly.

For e.g. if I have written a Amazon Lambda function to send a mail, I can use it from python as shown below:
from boto3 import client as boto3_client
lambda_client = boto3_client('lambda', region_name='us-east-1', aws_access_key_id='xx', aws_secret_access_key='xx')
x = {"title" : "test from lambda client invoke method", "email": "some.name@gmail.com"}
y=lambda_client.invoke(FunctionName="mymail", InvocationType='RequestResponse', Payload=json.dumps(x))
If you want to process the response of the function, then save the output to a variable like "y" and then use read method like this...
y['Payload'].read()
_____

You may need to create a new user with programatic access to lambda function. Attach this policy and generate access and secret keys.
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Action": ["lambda:InvokeFunction"],
      "Effect": "Allow",
      "Resource": "arn:aws:lambda:*:*:*"
    }
  ]
}
You can now share your access key with others who can invoke your lambda function from within another lambda function or code.

Labels: , ,


August 22, 2017

 

Reading large csv files in pandas dataframe

If you have very large csv files, we can not use pandas dataframe. We can use dask dataframe, but that will be slow. The best option is to convert csv to parquet using the following code.

import dask.dataframe as dd

df = dd.read_csv('csv/yellow_tripdata_2015-*.csv')

df.to_parquet('yellow_tripdata.parquet')


We can now read parquet file into a dataframe and the processing will be pretty fast.

df = dd.read_parquet('yellow_tripdata.parquet/')

df.passenger_count.sum().compute()

Labels: ,


August 10, 2017

 

frequently used pandas snippets

# replace special characters from column names:
df.columns=df.columns.str.replace("[^\w\s]+","").str.replace("\s+","_").str.lower()

# changing data-type compatible with Excel:
for ci in df.dtypes[df.dtypes == object].index:
    df[ci]=df[ci].str.encode('utf-8')

# dump a table data to a files:
!mysqldump test tbl --skip-extended-insert --insert-ignore --complete-insert --no-create-info --compact

# analyze dataframe

df.info()

df.describe().T

df.apply(lambda x: x.nunique())
_____

# function to remove non-english characters

import string
ascii = set(string.printable)

def remove_non_ascii(s):
    x=list(filter(lambda x: x in ascii, str(s)))
    return ''.join(x)

for i in  df.select_dtypes(['object']):
    df[i]=df[i].astype(str).apply(lambda x: remove_non_ascii(x))

# change the case to title case for all string columns
for i in df.select_dtypes(['object']):
    df[i]=df[i].astype(str).apply(lambda x: x.title())
_____

# connect to mysql and save a table as dataframe
from sqlalchemy import create_engine
engine = create_engine("mysql+pymysql://root:passwd@172.31.67.25:3397/test?charset=utf8")
df=pd.read_sql('select * from some_master ', con=engine)

# convert the entire dataframe to lowercase
df=df.apply(lambda x: x.astype(str).str.lower())

# display data type of each cell
terms.applymap(type)
_____

# find and compress all excel files
import glob
import zipfile
for i in glob.glob("*.xls*"):
    jungle_zip = zipfile.ZipFile(i+'.zip', 'w')
    jungle_zip.write( i , compress_type=zipfile.ZIP_DEFLATED)
    jungle_zip.close()

Labels: ,


July 11, 2017

 

manage duplicate items in a list

Here is how to rename duplicate items in a list.

mylist=['a', 'b', 'c', 'a', 'a']

As you can see the single value "a" is repeated 3 times.

from collections import Counter
mylist=[s + str(suffix) if num>1 else s for s,num in Counter(mylist).items() for suffix in range(1, num+1)]

mylist
['a1', 'a2', 'a3', 'b', 'c']

Labels: ,


Archives

June 2001   July 2001   January 2003   May 2003   September 2003   October 2003   December 2003   January 2004   February 2004   March 2004   April 2004   May 2004   June 2004   July 2004   August 2004   September 2004   October 2004   November 2004   December 2004   January 2005   February 2005   March 2005   April 2005   May 2005   June 2005   July 2005   August 2005   September 2005   October 2005   November 2005   December 2005   January 2006   February 2006   March 2006   April 2006   May 2006   June 2006   July 2006   August 2006   September 2006   October 2006   November 2006   December 2006   January 2007   February 2007   March 2007   April 2007   June 2007   July 2007   August 2007   September 2007   October 2007   November 2007   December 2007   January 2008   February 2008   March 2008   April 2008   July 2008   August 2008   September 2008   October 2008   November 2008   December 2008   January 2009   February 2009   March 2009   April 2009   May 2009   June 2009   July 2009   August 2009   September 2009   October 2009   November 2009   December 2009   January 2010   February 2010   March 2010   April 2010   May 2010   June 2010   July 2010   August 2010   September 2010   October 2010   November 2010   December 2010   January 2011   February 2011   March 2011   April 2011   May 2011   June 2011   July 2011   August 2011   September 2011   October 2011   November 2011   December 2011   January 2012   February 2012   March 2012   April 2012   May 2012   June 2012   July 2012   August 2012   October 2012   November 2012   December 2012   January 2013   February 2013   March 2013   April 2013   May 2013   June 2013   July 2013   September 2013   October 2013   January 2014   March 2014   April 2014   May 2014   July 2014   August 2014   September 2014   October 2014   November 2014   December 2014   January 2015   February 2015   March 2015   April 2015   May 2015   June 2015   July 2015   August 2015   September 2015   January 2016   February 2016   March 2016   April 2016   May 2016   June 2016   July 2016   August 2016   September 2016   October 2016   November 2016   December 2016   January 2017   February 2017   April 2017   May 2017   June 2017   July 2017   August 2017   September 2017  

This page is powered by Blogger. Isn't yours?