Importing Data in Python

# Add your code snippets here
#Ejemplo 1
# Import package
import numpy as np

# Assign filename: file
file = 'datasets/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()

#Ejemplo 2

# Assign the filename: file
file = 'datasets/titanic_sub.csv'

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

# 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))

#Ejemplo3 .txt
# 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()

# Assign spreadsheet filename: file
file = 'datasets/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('datasets/sales.sas7bdat') as file:
    df_sas = file.to_data_frame()

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

# Plot histogram of DataFrame features (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('datasets/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 = 'datasets/L-L1_LOSC_4_V1-1126259446-32.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 = np.array(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()