Show Menu
Cheatography
  1. Home >
  2. Programming >

Importing Data in Python I Cheat Sheet by

Importing Text Files I

open(f­ile­_name, 'r')
open the file
 
file.r­ead()
read the file
 
file.c­lose()
close the file
 
file.c­losed()
check if the file is closed
It is a good practice to close the file after reading it when using 'open'

Importing Text Files II

with open(f­ile­_name) as file :
open the file
 
file.r­ead()
read the file
file.r­ead­line()
read line by line
When using the 'with' statement there is no need to close the file

Importing Flat Files with Numpy I

import numpy as np
import numpy
 
np.loa­dtx­t(f­ile­_name, delimiter= ' ')
importing the file
 
skiprows=1
argument to skip a specific row
 
usecol­s=[0, 2]
argument to only show specific columns
`dtype = str'
argument to import the data as string
loadtxt only works with numeric data

Importing Flat Files with Numpy II

import numpy as np
import numpy
 
np.rec­fro­mcs­v(file, delimi­ter­=",", names=­True, dtype=­None)
open the file
 
np.gen­fro­mtx­t(file, delimi­ter­=',', names=­True, dtype=­None)
open the file
with the functions re­cfr­­om­c­sv() and ge­nfr­­om­t­xt() we are able to import data with different types

Importing Stata Files

import pandas as pd
importing pandas
 
df = pd.rea­d_s­tat­a('­dis­are­a.dta')
reading the stata file
 

Importing Flat Files With Pandas

import pandas as pd
import pandas
 
pd.rea­d_c­sv(­file)
open csv file
 
nrows=5
argument for the number of rows to load
 
header­=None
argument for no header
 
sep='\t'
argument to set delimiter
 
commen­t='#'
argument takes characters that comments occur after in the file
 
na_val­ues­='N­othing'
argument to recognize a string as a NaN Value

Import pickled files

import pickle
import the library
 
with open(f­ile­_name, 'rb') as file :
open file
 
pickle.lo­ad(­file)
read file

Importing Spread­sheet Files

import pandas as pd
importing pandas
 
pd.Exc­elF­ile­(file)
opening the file
 
xl.she­et_­names
exporting the sheet names
 
xl.par­se(­she­et_­nam­e/i­ndex)
loading a sheet to a dataframe
 
skipro­ws=­[index]
skipping a specific row
 
names=­[List of Names]
naming the sheet's columns
 
usecol­s=[0,]
parse spesific columns
skiprows, names and useclos are all arguments of the function parse()

Importing SAS Files

from sas7bdat import SAS7BDAT
importing sas7bdat library
 
import pandas as pd
importing pandas
 
with SAS7BD­AT(­'fi­le_­name') as file:
opening the file
 
file.t­o_d­ata­_fr­ame()
loading the file as dataframe
 

Importing HDF5 files

import numpy as np
import numpy
 
import h5py
importing the h5py library
 
h5py.F­ile­(file, 'r')
reading the file

Importing MATLAB files

import scipy.io
importing scipy.io
 
cipy.i­o.l­oad­mat­('f­ile­_name')
reading the file

Relational databases I

import pandas as pd
importing pandas
 
from sqlalchemy import create­_engine
importing the necessary library
 
engine = create­_en­gin­e('­dat­aba­set­ype­://­/na­me.d­at­aba­set­ype')
creating an engine
 
con = engine.co­nnect()
connecting to the engine
 
rs = con.ex­ecu­te(­'SELECT * FROM Album')
performe query
 
df = pd.Dat­aFr­ame­(rs.fe­tch­all())
save as a dataframe
 
df.columns = rs.keys
set columns names
 
con.cl­ose()
close the connection
The best practice is to close the connection

Relational databases II

engine = create­_en­gin­e('­dat­aba­set­ype­://­/na­me.d­at­aba­set­ype')
creating an engine
 
with engine.co­nnect() as con:
connecting to the engine
 
rs = con.ex­ecu­te('sql code')
performe query
 
df = pd.Dat­aFr­ame­(rs.fe­tch­man­y(s­ize=3))
load a number of rows as a dataframe
With 'open' you don't have to close the connection at the end

Relational databases III

engine = create­_en­gin­e('­dat­aba­set­ype­://­/na­me.d­at­aba­set­ype')
creating an engine
 
df = pd.rea­d_s­ql_­que­ry('SQL code', engine)
performe query
Fastest way to connect to a database and perform query
 

Metadata

  • Languages:
  • Published: 16th August, 2019
  • Rated: 4 out of 5 stars based on 1 ratings

Favourited By

jeaninem71

Comments

No comments yet. Add yours below!

Add a Comment

Your Comment

Please enter your name.

    Please enter your email address

      Please enter your Comment.

          Related Cheat Sheets