Mini-Project for T3 Wings - Exploratory Data Analysis Fresco Play Hands-on Solution HackerRank

Wings - Machine First Al - Exploratory Data Analysis

Instructions:

The data required for this task has been provided in the file 'data.csv'
Read the questions provided for each cell and assign your answers to respective variables provided in the following cell.
If answers are floating point numbers round of upto two floating point after the decimal. For example, 10.546 should be read as 10.55; 10.544 as 10.54 and 10.1 as 10.10
pandas and numpy packages are preinstalled and these packages should to sufficient to solve this task.
Please don't change variable name meant to assign your answers.

#Run this cell to import the Packages.
import pandas as pd
import numpy as np

### Read the data (this will not be graded)
df = pd.read_csv('data.csv')
df.head()

Task 1: What is the standard deviation of maximum windspeed across all the days.

Note: ws_std should be of type float.

ws_std =  round(df['Maximum windspeed (mph)'].std(),2)
ws_std

Task 2: What is the difference between 50th percentile and 75th percentile of average temperature.

Note: p_range should be of type float

p50 = np.percentile(df['Average temperature (°F)'], 50)
p75= df['Average temperature (°F)'].quantile(0.75)

p_range = float(round(p75-p50, 2))
p_range 
p50 = np.percentile(df['Average temperature (°F)'], 50)
p75= df['Average temperature (°F)'].quantile(0.75)

p_range = float(round(p75-p50, 2))
p_range

Task 3; What is the pearson correlation between average dew point and average temperature.

Note: corr should be of type float


Solution 1  
correlation_matrix = df[['Average temperature (°F)', 'Average dewpoint (°F)']].corr(method='pearson')
corr = float(round(correlation_matrix.loc['Average temperature (°F)', 'Average dewpoint (°F)'],2))
print(corr)


Solution B
import scipy.stats as stats
cor_coeff_sts, p_value = stats.pearsonr(df['Average temperature (°F)'], df['Average dewpoint (°F)'])
corr = float( round(cor_coeff_sts,2))
print(corr)

Task 4: Out of all the available records which month has the lowest average humidity.

- Assign your answer as month index, for example if its July index is 7. Note: dew_month should be of type int

df["Day"] = pd.to_datetime(df["Day"], format="%d/%m/%Y")

# Extract month
df["Month"] = df["Day"].dt.month

dew_month = int(df.groupby("Month")["Average humidity (%)"].min().idxmin())
dew_month

Task 5: Which month has the highest median for maximum_gust_speed out of all the available records. Also find the respective value - hint: group by month Note:

max_gust_value should be of type float
max_gust_month should be of type int
  
  
max_gust_value = float(round(df.groupby("Month")["Maximum gust speed (mph)"].median().max(),2))
max_gust_month = int(df.groupby("Month")["Maximum gust speed (mph)"].median().idxmax())

max_gust_value , max_gust_month

Task 6: Determine the average temperature between the months of March 2010 to May 2012 (including both the months) Note: avg_temp should be of type float

df2 = df.set_index("Day", inplace=False)
avg_temp = float(round(df2.loc["2010-03":"2012-05"]['Average temperature (°F)'].mean(),2))
avg_temp

Task 7: Find the range of average temperature on Dec 2010 Note: temp_range should be of type float

  temp_range = float(round(df2.loc["2010-12"]['Average temperature (°F)'].agg(["min", "max"]).diff().iloc[-1],2))
temp_range

Task 8: Out of all available records which day has the highest difference between maximum_pressure and minimum_pressure - assign the date in string format as 'yyyy-mm-dd'. Make sure you enclose it with single quote

df2['Pressure_Diff'] = df2['Maximum pressure '] - df2['Minimum pressure ']
max_p_range_day = df2['Pressure_Diff'].idxmax().strftime('%Y-%m-%d')   # f"'{df2['Pressure_Diff'].idxmax().strftime('%Y-%m-%d')}'"
print( max_p_range_day)

Task 9: How many days falls under median (i.e equal to median value) of barometer reading. Note: median_b_days should be of type int

median_b_days = int((df['Average barometer (in)'] == df['Average barometer (in)'].median()).sum())
median_b_days

Task 10: Out of all the available records how many days are within one standard deviation of average temperature Note: num_days_std should be of type int

m, s =  df['Average temperature (°F)'].agg(['mean','std'])
ub = m+s
lb = m-s
print(ub, lb)

num_days_std = int(df[(df['Average temperature (°F)']>=lb) & (df['Average temperature (°F)']<=ub)].shape[0])
num_days_std

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