To perform Univariate Analysis for the given data set.
Univariate analysis is basically the simplest form to analyze data. Uni means one and this means that the data has only one kind of variable. The major reason for univariate analysis is to use the data to describe. The analysis will take data, summarise it, and then find some pattern in the data
Read the given data.
Get the information about the data.
Perform Univariate Analysis Techniques for the given dataset.
Save the data to the file.
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
df.info()
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
df.dtypes
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
df['Row ID'].value_counts()
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
df['Order ID'].value_counts()
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
df.describe()
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
df.skew()
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
df.kurtosis()
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
sns.boxplot(x="Row ID",data=df)
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
sns.countplot(x="Row ID",data=df)
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
sns.distplot(df["Row ID"])
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("SuperStore.csv")
sns.histplot(x="Row ID",data=df)
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
df.info()
print("\n")
df.dtypes
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
df['Glucose'].value_counts()
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
df['BloodPressure'].value_counts()
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
df.describe()
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
df.skew()
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
df.kurtosis()
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
sns.boxplot(x="Glucose",data=df)
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
sns.countplot(x="Glucose",data=df)
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
sns.distplot(df["Glucose"])
import pandas as pd
import seaborn as sns
from google.colab import files
uploaded = files.upload()
df = pd.read_csv("diabetes.csv")
sns.histplot(x="Glucose",data=df)
Thus the Univariate Analysis for the given data set is executed and output was verified successfully.