code.py

#Importing the modules
import pandas as pd
import numpy as np
from scipy.stats import mode 

#Code for categorical variable

def categorical(df):
    """ Extract names of categorical column
    
    This function accepts a dataframe and returns categorical list,
    containing the names of categorical columns(categorical_var).
    
    Keyword arguments:
    df - Pandas dataframe from which the columns name will be extracted
        
    Returns:
    categorical_var - List of categorical features
    """
    categorical_var=df.select_dtypes(include ='object').columns.tolist()

    return categorical_var


#Code for numerical variable

def numerical(df):
    """ Extract names of numerical column
    
    This function accepts a dataframe and returns numerical list,
    containing the names of numerical columns(numerical_var).
        
    Keyword arguments:
    df - Pandas dataframe from which the columns name will be extracted
    
    Returns:
    numerical_var - List of numerical features
    """
    numerical_var=df.select_dtypes(include =['float64','int64']).columns.tolist()
    return numerical_var


#code to check distribution of variable

def clear(df,col,val):
    """ Check distribution of variable
    
    This function accepts a dataframe,column(feature) and value which returns count of the value,
    containing the value counts of a variable(value_counts)
    
    Keyword arguments:
    df - Pandas dataframe
    col - Feature of the datagrame
    val - value of the feature
    
    Returns:
    value_counts - Value count of the feature 
    """
    value_counts=df[col][df[col]==val].count()
    return value_counts
   

#Code to check instances based on the condition

def instances_based_condition(df,col1,val1,col2,val2):
    """ Instances based on the condition
    
    This function accepts a dataframe, 2 columns(feature) and 2 values which returns the dataframe
    based on the condition.
    
    Keyword arguments:
    df - Pandas dataframe which has the data.
    col1 - First feature of the dataframe on which you want to apply the filter
    val1 - Value to be filtered on the first feature
    col2 - Second feature of the dataframe on which you want to apply the filter
    val2 - Value to be filtered on second feature
    
    Returns:
    instance - Generated dataframe
    """
    instance=df[(df[col1]>val1) & (df[col2]==val2)]
    return instance
    

# Code to calculate different aggreagted values according to month

def agg_values_ina_month(df,date_col,agg_col, agg):
    """  Aggregate values according to month
    
    This function accepts a dataframe, 2 columns(feature) and aggregated funcion(agg) which returns the Pivot 
    table with different aggregated value of the feature with an index of the month.
     
    Keyword arguments:
    df - Pandas dataframe which has the data.
    date_col - Date feature of the dataframe on which you want to apply to_datetime conversion
    agg_col - Feature of the dataframe on which values will be aggregated.
    agg - The function to be used for aggregating the df (eg. 'mean', 'min', 'max').
    
    Returns:
    aggregated_value - Generated pivot table
    """
    df[date_col] = pd.to_datetime(df[date_col])
    
    aggregate = {'mean':np.mean,'max':np.max,'min':np.min,'sum':np.sum,'len':len}
    
    aggregated_value = df.pivot_table(values=[agg_col], index=df[date_col].dt.month,aggfunc={agg_col:aggregate[agg]})
    
    return aggregated_value


# Code to group values based on the feature

def group_values(df,col1,agg1):
    """ Agrregate values by grouping
    
    This function accepts a dataframe, 1 column(feature) and aggregated function(agg1) which groupby the 
    datframe based on the column.
   
   Keyword arguments:
    df - Pandas dataframe which has the data.
    col1 - Feature of the dataframe on which values will be aggregated.
    agg1 - The function to be used for aggregating the df (eg. 'mean', 'min', 'max').
    
    Returns:
    grouping - Dataframe with all columns on which it is grouped on.
    """
    grouping=df.groupby(col1).agg(agg1)
    return grouping
    

# function for convert temperatures

def convert(df,celsius):
    """ Convert temperatures from celsius to fahrenhheit
    
    This function accepts a dataframe, 1 column(feature) which returns the dataframe with converted values from 
    celsius to fahrenhheit.
         
    Keyword arguments:
    df - Pandas dataframe which has the data.
    celsius - Temperature feature of the dataframe which you want to convert to fahrenhheit
    
    Returns:
    converted_temp - Generated dataframe with Fahrenhheit temp.
    
    """
    converted_temp=(df[celsius]*(9/5))+32
    return converted_temp


#1 Load the weather_2012 data csv file and storing it in weather variable.

weather=pd.read_csv(path)
print(weather.head())

print(weather.info())

#2 Checking the categorical and numerical variables by calling categorical and numerical function. 

print(categorical(weather))

print(numerical(weather))


#3 Checking the distribution of a specific value like the number of times the weather was exactly Cloudy in the given column.

#Checking it by calling the function clear with respective parameters.

#By using index of the value or name of the value, we can check the number of count
print(clear(weather,'Weather','Cloudy'))

#4 Now suppose we want to check some instances based on a specific condition like when the wind speed was above 35 and visibility was 25. 

#Checking it by calling the function instances_based_condition with respective parameters.
wind_speed_35_vis_25=instances_based_condition(weather,'Wind Spd (km/h)',35,'Visibility (km)',25)

print(wind_speed_35_vis_25.head())

#5 We have temperature data and want to calculate the mean temperature recorded by month.

#generating a pivot table which contains the aggregated values(like mean, max ,min, sum, len) recoreded by month. 

#calling the function agg_values_ina_month with respective parameters. 
agg_values_ina_month(weather,'Date/Time','Dew Point Temp (C)','mean')

print(agg_values_ina_month(weather,'Date/Time','Dew Point Temp (C)','mean'))


#6 To groupby based on a column like you want to groupby on Weather column and then aggregate the mean values of each column for different types of weather using mean. 

#calling the function group_values with aggregated functions like max, min, sum, len.
mean_weather=group_values(weather,'Weather','mean')

#7 We have a temperature data and wanted to convert celsius temperature into fahrehheit temperatures 

#calling the function convert.
weather_fahrehheit=convert(weather,'Temp (C)')