Seaborn + Pandas
What is the pandas
?
- Python library for data analysis;
- Easily reads DataSets from csv, txt, and other types of files;
- DataSets take the form of DataFrame objects.
When you read a dataset with the help of the pandas
you create a DataFrame:
# Importing the pandas
import pandas as pd
# Reading the file
df = pd.read_csv('file.csv')
To initialize a countplot based on the pandas
DataFrame, we need to input at least 2 parameters: x
(the column whose values will be counted) and data
(the DataFrame containing the data).
Look at the code below!
123456789101112131415# Importing the Seaborn import seaborn as sns # Importing the matplotlib.pyplot import matplotlib.pyplot as plt # Importing the pandas import pandas as pd # Reading the file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c5b4ea8f-8a30-439f-9625-ddf2effbd9ac/example3.csv') # Creating the countplot sns.countplot(x = 'food', data = df) # Showing the plot plt.show()
Let's solve this problem!
Swipe to start coding
-
Import the
seaborn
with thesns
alias. -
Import the
matplotlib.pyplot
with theplt
alias. -
Import the
pandas
with thepd
alias. -
Read the file using
df
variable. -
Create the x-oriented countplot using the
'Bamboo'
column in the plot function. -
Show the plot.
Ratkaisu
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Completion rate improved to 5.88Awesome!
Completion rate improved to 5.88
Seaborn + Pandas
What is the pandas
?
- Python library for data analysis;
- Easily reads DataSets from csv, txt, and other types of files;
- DataSets take the form of DataFrame objects.
When you read a dataset with the help of the pandas
you create a DataFrame:
# Importing the pandas
import pandas as pd
# Reading the file
df = pd.read_csv('file.csv')
To initialize a countplot based on the pandas
DataFrame, we need to input at least 2 parameters: x
(the column whose values will be counted) and data
(the DataFrame containing the data).
Look at the code below!
123456789101112131415# Importing the Seaborn import seaborn as sns # Importing the matplotlib.pyplot import matplotlib.pyplot as plt # Importing the pandas import pandas as pd # Reading the file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c5b4ea8f-8a30-439f-9625-ddf2effbd9ac/example3.csv') # Creating the countplot sns.countplot(x = 'food', data = df) # Showing the plot plt.show()
Let's solve this problem!
Swipe to start coding
-
Import the
seaborn
with thesns
alias. -
Import the
matplotlib.pyplot
with theplt
alias. -
Import the
pandas
with thepd
alias. -
Read the file using
df
variable. -
Create the x-oriented countplot using the
'Bamboo'
column in the plot function. -
Show the plot.
Ratkaisu
Kiitos palautteestasi!
single
Awesome!
Completion rate improved to 5.88
Seaborn + Pandas
Pyyhkäise näyttääksesi valikon
What is the pandas
?
- Python library for data analysis;
- Easily reads DataSets from csv, txt, and other types of files;
- DataSets take the form of DataFrame objects.
When you read a dataset with the help of the pandas
you create a DataFrame:
# Importing the pandas
import pandas as pd
# Reading the file
df = pd.read_csv('file.csv')
To initialize a countplot based on the pandas
DataFrame, we need to input at least 2 parameters: x
(the column whose values will be counted) and data
(the DataFrame containing the data).
Look at the code below!
123456789101112131415# Importing the Seaborn import seaborn as sns # Importing the matplotlib.pyplot import matplotlib.pyplot as plt # Importing the pandas import pandas as pd # Reading the file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c5b4ea8f-8a30-439f-9625-ddf2effbd9ac/example3.csv') # Creating the countplot sns.countplot(x = 'food', data = df) # Showing the plot plt.show()
Let's solve this problem!
Swipe to start coding
-
Import the
seaborn
with thesns
alias. -
Import the
matplotlib.pyplot
with theplt
alias. -
Import the
pandas
with thepd
alias. -
Read the file using
df
variable. -
Create the x-oriented countplot using the
'Bamboo'
column in the plot function. -
Show the plot.
Ratkaisu
Kiitos palautteestasi!