Introduction to Importing Data in Python

# Import the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import h5py
from sas7bdat import SAS7BDAT
from sqlalchemy import create_engine
import pickle

# Import the course datasets
titanic = pd.read_csv("datasets/titanic_sub.csv")
battledeath_2002 = pd.ExcelFile("datasets/battledeath.xlsx").parse("2002")
engine = create_engine('sqlite:///datasets/Chinook.sqlite')
con = engine.connect()
rs = con.execute('SELECT * FROM Album')
chinook = pd.DataFrame(rs.fetchall())
seaslug = np.loadtxt("datasets/seaslug.txt", delimiter="\t", dtype=str)

# Reading a text file
filename = 'huck_finn.txt'
file = open(filename, mode='r') #'r' is for read
text = file.read()
file.close()
print(text)

# Context manager 'with'
with open('huck_finn.txt','r') as file:
    print(file.read())
    
# Open a file: file
file = open('moby_dick.txt', 'r')

# Print it
print(file.read())

# Check whether file is closed
print(file.closed)

# Close file
file.close()

# Check whether file is closed
print(file.closed)

# Read & print the first 3 lines
with open('moby_dick.txt') as file:
    print(file.readline())
    print(file.readline())
    print(file.readline())

# Import package
import numpy as np

# Assign filename to variable: file
file = 'digits.csv'

# Load file as array: digits
digits = np.loadtxt(file, delimiter=',')

# Print datatype of digits
print(type(digits))

# Select and reshape a row
im = digits[21, 1:]
im_sq = np.reshape(im, (28, 28))

# Plot reshaped data (matplotlib.pyplot already loaded as plt)
plt.imshow(im_sq, cmap='Greys', interpolation='nearest')
plt.show()

# Import numpy
import numpy as np

# Assign the filename: file
file = 'digits_header.txt'

# Load the data: data
data = np.loadtxt(file, delimiter="\t", skiprows= 1, usecols=[0,2])

# Print data
print(data)

# Assign filename: file
file = 'seaslug.txt'

# Import file: data
data = np.loadtxt(file, delimiter='\t', dtype=str)

# Print the first element of data
print(data[0])

# Import data as floats and skip the first row: data_float
data_float = np.loadtxt(file, delimiter='\t', dtype=float, skiprows=1)

# Print the 10th element of data_float
print(data_float[9])

# Plot a scatterplot of the data
plt.scatter(data_float[:, 0], data_float[:, 1])
plt.xlabel('time (min.)')
plt.ylabel('percentage of larvae')
plt.show()

# Assign the filename: file
file = 'titanic.csv'

# Import file using np.recfromcsv: d
d = np.recfromcsv(file)

# Print out first three entries of d
print(d[:3])

# Assign the filename: file
file = 'digits.csv'

# Read the first 5 rows of the file into a DataFrame: data
data = pd.read_csv(file, nrows=5, header=None)

# Build a numpy array from the DataFrame: data_array
data_array = data.values

# Print the datatype of data_array to the shell
print(type(data_array))

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Assign filename: file
file = 'titanic_corrupt.txt'

# Import file: data
data = pd.read_csv(file, sep='\t', comment='#', na_values='Nothing')

# Print the head of the DataFrame
print(data.head())

# Plot 'Age' variable in a histogram
pd.DataFrame.hist(data[['Age']])
plt.xlabel('Age (years)')
plt.ylabel('count')
plt.show()

# Import pickle package
import pickle

# Open pickle file and load data
with open('data.pkl', 'rb') as file:
    d = pickle.load(file)

# Print data
print(d)

# Print datatype
print(type(d))

# Import pandas
import pandas as pd

# Assign spreadsheet filename: file
file = 'battledeath.xlsx'

# Load spreadsheet: xls
xls = pd.ExcelFile(file)

# Print sheet names
print(xls.sheet_names)

# Load a sheet into a DataFrame by name: df1
df1 = xls.parse('2004')

# Print the head of the DataFrame df1
print(df1.head())

# Load a sheet into a DataFrame by index: df2
df2 = xls.parse(0)

# Print the head of the DataFrame df2
print(df2.head())

# Parse the first sheet and rename the columns: df1
df1 = xls.parse(0, skiprows=[0], names=['Country', 'AAM due to War (2002)'])

# Print the head of the DataFrame df1
print(df1.head())

# Parse the first column of the second sheet and rename the column: df2
df2 = xls.parse(1, usecols=[0], skiprows=[0], names=['Country'])

# Print the head of the DataFrame df2
print(df2.head())

# Import sas7bdat package
from sas7bdat import SAS7BDAT

# Save file to a DataFrame: df_sas
with SAS7BDAT('sales.sas7bdat') as file:
    df_sas = file.to_data_frame()

# Print head of DataFrame
print(df_sas.head())

# Plot histograms of a DataFrame feature (pandas and pyplot already imported)
pd.DataFrame.hist(df_sas[['P']])
plt.ylabel('count')
plt.show()

# Import pandas
import pandas as pd

# Load Stata file into a pandas DataFrame: df
df = pd.read_stata('disarea.dta')

# Print the head of the DataFrame df
print(df.head())

# Plot histogram of one column of the DataFrame
pd.DataFrame.hist(df[['disa10']])
plt.xlabel('Extent of disease')
plt.ylabel('Number of countries')
plt.show()

# Import packages
import numpy as np
import h5py

# Assign filename: file
file = 'LIGO_data.hdf5'

# Load file: data
data = h5py.File(file, 'r')

# Print the datatype of the loaded file
print(type(data))

# Print the keys of the file
for key in data.keys():
    print(key)
    
# Get the HDF5 group: group
group = data['strain']

# Check out keys of group
for key in group.keys():
    print(key)

# Set variable equal to time series data: strain
strain = data['strain']['Strain']

# Set number of time points to sample: num_samples
num_samples = 10000

# Set time vector
time = np.arange(0, 1, 1/num_samples)

# Plot data
plt.plot(time, strain[:num_samples])
plt.xlabel('GPS Time (s)')
plt.ylabel('strain')
plt.show()

# Import package
import scipy.io

# Load MATLAB file: mat
mat = scipy.io.loadmat('albeck_gene_expression.mat')

# Print the datatype type of mat
print(type(mat))

# Print the keys of the MATLAB dictionary
print(mat.keys())

# Print the type of the value corresponding to the key 'CYratioCyt'
print(type(mat['CYratioCyt']))

# Print the shape of the value corresponding to the key 'CYratioCyt'
print(np.shape(mat['CYratioCyt']))

# Subset the array and plot it
data = mat['CYratioCyt'][25, 5:]
fig = plt.figure()
plt.plot(data)
plt.xlabel('time (min.)')
plt.ylabel('normalized fluorescence (measure of expression)')
plt.show()

# Import necessary module
from sqlalchemy import create_engine

# Create engine: engine
engine = create_engine('sqlite:///Chinook.sqlite')

# Import necessary module
from sqlalchemy import create_engine

# Create engine: engine
engine = create_engine('sqlite:///Chinook.sqlite')

# Save the table names to a list: table_names
table_names = engine.table_names()

# Print the table names to the shell
print(table_names)

# Open engine in context manager
# Perform query and save results to DataFrame: df
with engine.connect() as con:
    rs = con.execute("SELECT LastName, Title FROM Employee")
    df = pd.DataFrame(rs.fetchmany(size=3))
    df.columns = rs.keys()

# Print the length of the DataFrame df
print(len(df))

# Print the head of the DataFrame df
print(df.head())

# Create engine: engine
engine = create_engine('sqlite:///Chinook.sqlite')

# Open engine in context manager
# Perform query and save results to DataFrame: df
with engine.connect() as con:
    rs = con.execute("SELECT * FROM Employee WHERE EmployeeId >= 6")
    df = pd.DataFrame(rs.fetchall())
    df.columns = rs.keys()

# Print the head of the DataFrame df
print(df.head())

# Create engine: engine
engine = create_engine('sqlite:///Chinook.sqlite')

# Open engine in context manager
with engine.connect() as con:
    rs = con.execute("SELECT * FROM Employee ORDER BY BirthDate")
    df = pd.DataFrame(rs.fetchall())

    # Set the DataFrame's column names
    df.columns = rs.keys()

# Print head of DataFrame
print(df.head())

# Import packages
from sqlalchemy import create_engine
import pandas as pd

# Create engine: engine
engine = create_engine('sqlite:///Chinook.sqlite')

# Execute query and store records in DataFrame: df
df = pd.read_sql_query("SELECT * FROM Album", engine)

# Print head of DataFrame
print(df.head())

# Open engine in context manager
# Perform query and save results to DataFrame: df
with engine.connect() as con:
    rs = con.execute("SELECT Title, Name FROM Album INNER JOIN Artist on Album.ArtistID = Artist.ArtistID")
    df = pd.DataFrame(rs.fetchall())
    df.columns = rs.keys()

# Print head of DataFrame df
print(df.head())

# Execute query and store records in DataFrame: df
df = pd.read_sql_query("SELECT * FROM PlaylistTrack INNER JOIN Track on PlaylistTrack.TrackId = Track.TrackId WHERE Milliseconds < 250000", engine)

# Print head of DataFrame
print(df.head())