Question

######################LANGUAGE PANDAS####################

#####################MATPLOTLIB###########################

#########################################################

# read ufo.csv into a DataFrame called 'ufo'

# print the head and the tail

# examine the default index, data types, and shape of ufo dataframe

# count the number of missing values in each column

# count total number of null vlaues in the dataframe

# print those rows which has null values

# fill null values,
#if any column is numerical has null value than fill this column with mean of that column

#if any column is categorical than fill this column with most frequent value of that column

# calculate the most frequent value for each of the columns (in a single command)

# what are the four most frequent colors reported?

# for reports in VA, what's the most frequent city?

# show only the UFO reports from Arlington, VA

# show only the UFO reports in which the City is missing

# how many rows remain if you drop all rows with any missing values?

# replace any spaces in the column names with an underscore

# create a new column called 'Location' that includes both City and State
# For example, the 'Location' for the first row would be 'Ithaca, NY'

# map existing values to a different set of values
# like in column 'is_male', convert F value to 0 and M to 1 with pandas

# writing generic code to replace spaces with underscores
# In other words, your code should not reference the specific column names

# convert datatype of column 'time' to the datetime format

ufo.csv file

City	Colors Reported	Shape Reported	State	Time
Ithaca		TRIANGLE	NY	########
Willingboro		OTHER	NJ	########
Holyoke		OVAL	CO	########
Abilene		DISK	KS	########
New York Worlds Fair		LIGHT	NY	########
Valley City		DISK	ND	########
Crater Lake		CIRCLE	CA	########
Alma		DISK	MI	########
Eklutna		CIGAR	AK	########
Hubbard		CYLINDER	OR	########
Fontana		LIGHT	CA	########
Waterloo		FIREBALL	AL	########
Belton	RED	SPHERE	SC	########
Keokuk		OVAL	IA	########
Ludington		DISK	MI	########
Forest Home		CIRCLE	CA	########
Los Angeles			CA	########
Hapeville			GA	########
Oneida		RECTANGLE	TN	########
Bering Sea	RED	OTHER	AK	########
Nebraska		DISK	NE	########

Answer 1

# Importting libraries
import numpy as np
import pandas as pd
import matplotlib as plt

#

# Reading csv using read_csv method from pandas library
ufo = pd.read_csv("ufo.csv")
ufo

#

# Print the head i.e top 5 rows of ufo dataset
ufo.head()
# Print the tail i.e last 5 rows of ufo dataset
ufo.tail()

#

# Examine default index
ufo.index
# Data types of the data
ufo.dtypes
# Shape of the dataframe(rows, columns)
ufo.shape

#

# count the number of missing values in each column
ufo.isnull().sum()

#

# count total number of null vlaues in the dataframe
print ufo.isnull().sum().sum()

#

# print those rows which has null values
ufo[ufo.isnull().any(axis=1)]

#

#if any column is categorical than fill this column with most frequent value of that column
ufo = ufo.apply(lambda x:x.fillna(x.value_counts().index[0]))

#

# calculate the most frequent value for each of the columns (in a single command)
for each in ufo:print ufo[each].value_counts()[ufo[each].value_counts() == ufo[each].value_counts().max()]

#

#  what are the four most frequent colors reported?
ufo['Colors Reported'].value_counts()[0:4]

#

# for reports in VA, what's the most frequent city?
ufo_CA = ufo[ufo['State'] == "VA"]
ufo_CA["City"].value_counts()[ufo_CA["City"].value_counts() == ufo_CA["City"].value_counts().max()]

#

# show only the UFO reports from Arlington, VA
ufo[(ufo['State'] == "VA")&(ufo['City'] == "Arlington")]

#

# show only the UFO reports in which the City is missing
ufo[ufo['City'].isnull()]

#

# how many rows remain if you drop all rows with any missing values?
ufo = ufo.dropna()
len(ufo)

#

# replace any spaces in the column names with an underscore
ufo.columns = ufo.columns.str.replace(' ', '_') 
ufo

#

# map existing values to a different set of values
values_update = {"Shape_Reported":{"Circle": 4, "RECTANGLE": 2}}
ufo.replace(values_update, inplace=True)

#

# convert datatype of column 'time' to the datetime format
ufo['Time'] = pd.to_datetime(ufo['Time'])