import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings ('ignore')
#Data pre-pocesssing
#load the data using pandas read function
#print the dataset information
#print the statistics about the data using describe function
#visualize the correlation map to understand the correlation with the columns.
#check for null values, and if the data contains any, remove them.
#additionally, inspect for duplicate values and remove them if present.
#load the dataset and print the top 5 rows
import pandas as pd
data = pd.read_csv(r"C:\Users\user\Documents\DATA ANALYTICS\COVID DATA -SEUN.csv")
data.head()
States Affected No. of Cases (Lab Confirmed) No. of Cases (on admission) No. Discharged No. of Deaths
0 Lagos 104,118 975 102,372 771
1 FCT 29,492 38 29,205 249
2 Rivers 18,093 48 17,890 155
3 Kaduna 11,603 4 11,510 89
4 Oyo 10,334 2 10,130 202
#About the dataset
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 37 entries, 0 to 36
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 States Affected 37 non-null object
1 No. of Cases (Lab Confirmed) 37 non-null object
2 No. of Cases (on admission) 37 non-null int64
3 No. Discharged 37 non-null object
4 No. of Deaths 37 non-null int64
dtypes: int64(2), object(3)
memory usage: 1.6+ KB
#understanding the statistics in the dataset
data.describe().style.background_gradient(cmap='tab20c')
No. of Cases (on admission) No. of Deaths
count 37.000000 37.000000
mean 94.594595 85.270270
std 199.791900 134.897008
min -1.000000 2.000000
25% 2.000000 25.000000
50% 10.000000 38.000000
75% 67.000000 89.000000
max 975.000000 771.000000
#checking the correlaton matrix
plt.figure(figsize=(10,4))
sns.heatmap(data.corr(),annot=True,cmap='winter_r',fmt='.2f',linewidths=1)
plt.title('correlation map')
plt.show()
#data cleaning process
#checking the null values in the datasets
data.isna().sum()/len(data)*100
States Affected 0.0
No. of Cases (Lab Confirmed) 0.0
No. of Cases (on admission) 0.0
No. Discharged 0.0
No. of Deaths 0.0
dtype: float64
#checking the percentage of the null values in the dataset
null_values=data.isna().sum()
total_shells=np.product(data.shape)
total_missing_values=null_values.sum()
percentage_missing_values=(total_missing_values/total_shells)*100
print(f'the dataset contains {percentage_missing_values} of values')
the dataset contains 0.0 of values
#checking the duplicate values in the dataset
duplicate=data.duplicated().sum()
print(f'there is {duplicate} values in the dataset')
there is 0 values in the dataset
# Check column names and data types
print(data.dtypes)
States Affected object
No. of Cases (Lab Confirmed) object
No. of Cases (on admission) int64
No. Discharged object
No. of Deaths int64
dtype: object
#lets find number of the states affected in the dataset using the bar chart
data['States Affected'].value_counts().nlargest(10).plot(kind='bar', color=['#A9E2F3'])
plt.title('Top 10 states in the dataset')
plt.xlabel('states')
plt.xticks(rotation=90)
plt.ylabel('count of values')
plt.show()
