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Importing Text Files Iopen(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 IIwith 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 Iimport 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 IIimport 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 Filesimport pandas as pd
| importing pandas | df = pd.read_stata('disarea.dta')
| reading the stata file |
| | Importing Flat Files With Pandasimport 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 filesimport pickle
| import the library | with open(file_name, 'rb') as file :
| open file | pickle.load(file)
| read file |
Importing Spreadsheet Filesimport 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 Filesfrom 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 filesimport numpy as np
| import numpy | import h5py
| importing the h5py library | h5py.File(file, 'r')
| reading the file |
Importing MATLAB filesimport scipy.io
| importing scipy.io | cipy.io.loadmat('file_name')
| reading the file |
Relational databases Iimport 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 IIengine = 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 IIIengine = 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 |
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