weather_data_python

### Load the Data
weather_data = pd.DataFrame(pd.read_csv('WeatherUS_2016 (1).csv'))
weather_data.head()
weather_data.shape
weather_data.info()
weather_data.describe

#Find the duplicates
weather_data.duplicated().sum()

#unique values
weather_data['Type'].unique()

#unique values
weather_data['Severity'].unique()

#unique values
weather_data['State'].unique()

### Visualize the Unique counts
sns.countplot(weather_data['Type']).unique()

### Find the Null values
weather_data.isnull().sum()

### Correlation Plot - EDA
weather_data.corr()

### Removing unnecessary columns
Reduced_df = weather_data.drop(['Unnamed: 0', 'EventId','TimeZone','LocationLat','LocationLng','County','ZipCode'], axis=1)
Reduced_df.head()

### Rename the coolumns
Reduced_df.rename(
    columns={"StartTime(UTC)": "StartTime_UTC", "EndTime(UTC)": "EndTime_UTC", "Precipitation(in)": "Precipitation_in"},
    inplace=True,
)
Reduced_df.head()