Title
How do you read a CSV file into a pandas DataFrame? To read a CSV file into a pandas DataFrame, you can use the read_csv function in pandas.
How do you select a single column from a pandas DataFrame? To select a single column from a pandas DataFrame, you can use the indexing operator [] with the column name
How do you filter rows in a pandas DataFrame based on a condition? To filter rows in a pandas DataFrame based on a condition, you can use boolean indexing.
How do you group rows in a pandas DataFrame by a particular column? To group rows in a pandas DataFrame by a particular column, you can use the groupby method.
How do you aggregate data in a pandas DataFrame using functions like sum and mean? To aggregate data in a pandas DataFrame using functions like sum and mean, you can use the agg method.
How do you handle missing values in a pandas DataFrame? To handle missing values in a pandas DataFrame, you can use the fillna method to fill in missing values with a specific value or method, or you can use the dropna method to remove rows with missing values.
How do you merge two pandas DataFrames together? To merge two pandas DataFrames together, you can use the merge method
How do you export a pandas DataFrame to a CSV file? To export a pandas DataFrame to a CSV file, you can use the to_csv method
What is the difference between a Series and a DataFrame in Pandas? The main difference between a Series and a DataFrame in pandas is that a Series is a one-dimensional array-like object, while a DataFrame is a two-dimensional table-like data structure. A Series can be thought of as a single column of a DataFrame, while a DataFrame can have multiple columns.
import matplotlib.pyplot as plt
# Data for the chart
players = ['Jamie Vardy', 'Danny Ings', 'Pierre-Emerick Aubameyang', 'Raheem Shaquille Sterling',
'Mohamed Salah Ghaly', 'Sadio Mané', 'Harry Kane', 'Raúl Alonso Jiménez Rodríguez',
'Marcus Rashford']
goals = [23, 22, 22, 20, 19, 18, 18, 17, 17]
# Create the chart
plt.barh(players, goals, color='blue')
# Customize the chart
plt.title('Top Premier League Scorers 2019-2020')
plt.xlabel('Number of Goals')
plt.ylabel('Player')
# Display the chart
plt.show()
from skimage import io
import matplotlib.pyplot as plt
photo = io.imread('images/waldo.png')
type(photo)
plt.imshow(photo)
plt.imshow(photo[110:200, 225:300])
photo = io.imread('images/waldo.png')
type(photo)
plt.imshow(photo)
photo.shape
plt.imshow(photo[100:300, 350:400])
