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Introduction to Importing Data in Python

Run the hidden code cell below to import the data used in this course.

1 hidden cell

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

Add your notes here

# Add your code snippets here

Explore Datasets

Try importing the remaining files to explore the data and practice your skills!

  • datasets/disarea.dta
  • datasets/ja_data2.mat
  • datasets/L-L1_LOSC_4_V1-1126259446-32.hdf5
  • datasets/mnist_kaggle_some_rows.csv
  • datasets/sales.sas7bdat
# Import pandas as pd
import pandas as pd

# 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=['NA / NaN'])

# 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()
Hidden output

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()

# 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()

# 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()