Space Missions from 1957
Well, It all began with Man’s Curiosity.
The Term “Curiosity” when expressed in relation to man shouldn’t be taken with levity. It is that drive for answers that lies within man’s consciousness and subconsciousness. It revolves around every decision, action, and events ever carried out by man. It even reflects in our speech regardless of the language used. We ask “WHAT?” when we are unsure of the nature of something. We ask “WHY?” when we don’t know the reason an action was taken. We ask “HOW?” when we want to know the process by which an event occurred.
With curiosity man has gained and discovered so much in all fields you can think of, is it Medicine, infrastructure, Technology or Science? Think of anything, even beyond the scope of your immediate surroundings.
Speaking of beyond, man has expanded his scope of learning beyond “Earth”. Which birthed “SPACE EXPLORATION”, and that’s what this article is basically about.
Space is the boundless three-dimensional extent in which objects and events have relative position and direction. In classical physics, physical space is often conceived in three linear dimensions, although modern physicists usually consider it, with time, to be part of a boundless four-dimensional continuum known as spacetime. — Wikipedia.
Space was first explored by humans on Friday, October 4, 1957. When the Union of Soviet Socialist Republics (RVSN U.S.S.R) launched “Sputnik 8K71PS | Sputnik-1”, the first artificial satellite to orbit Earth. After which several other companies from various part of earth joined-in in the mission. It is important to point out that just as some missions were successful, some missions failed. Seems man’s curiosity always find a way to prevail, because that did not stop the exploration. It is fun to know that a Mars Rover launched from Cape Canaveral (CCAFS) on the 26th November, 2011 was actually named “Curiosity”
A data set was provided by Agirlcoding, Consultant at Deloitte, which provides a data list of the space missions right from the start of Space Exploration. This data set can be gotten here.
A proper scrutiny is a prerequisite to understand this data set. Data Analysis, Data Visualization and Data wangling can be used to achieve this.
To properly analyze the data set we need to do some sought of cleaning and re-arrangement to prepare the data for visualization and then analysis. Data Wangling is the proper method to do this. Let’s Begin!.
Python, an interpreted, object-oriented, high-level programming language with its libraries will be used in this process. Before anything we have to first import “pandas” as pd.
import pandas as pd“Pd” serves as a object that calls functions coded in the pandas’ libraries. As an example pd.Series() is a function that takes in two parameters, in the form of lists. The first serves as the Data values while the other serves as the index. The pd.Series() Constructor allows the input of only the data values as lists and has a default value for the index if not included.
dt=pd.Series(["nebula","sirius","GLYC124","Andromeda"], index=[1,2,3,4])
print(dt)
Likewise pd.DataFrame() takes in parameters such as “data”, “index” and “columns” as lists. It also takes in a dictionary as a parameter.
Data = [["nebula","1964"],["sirius","2000"],["GLYC124","1675"],["Andromeda","1234"]]
indexes =[1,2,3,4]
column = ["Stars","Year"]
dt=pd.DataFrame(data=Data, index=indexes, columns=column)
print(dt)
indexes =[1,2,3,4]
dt=pd.DataFrame({"Stars":["nebula","sirius","GLYC124","Andromeda"],"Year":["1964","2000","1675","1234"]}, index=indexes)
print(dt)
Data is the integral part of analysis and often stored in files (CSV, Excel, JSON, XML, SQL etc). So pandas also has inbuilt support to load data from files as a Data Frame. CSV is the format used for the data set we want to use. Let’s import the “Space_Corrected” data set by calling the pd.read_csv() function, which will be represented with “df”. df.head(), outputs the first five rows of the data set when called.
df=pd.read_csv("Space_Corrected.csv")
df.head()
We can see from the first five rows that the data set is not well ordered. So the first thing to do is to check for similarities in the first and second columns. we can do that by calling the df.info() function to present the information about the data set.
df.info()
From the above, we can see that the first and second columns actually have the same number and values of input. We need to make the data set simpler by removing one of them. Since they are similar, we do this by calling the drop function and passing in the required parameters.
df=pd.read_csv("Space_Corrected.csv")
if df['Unnamed: 0.1'].unique().all() == df['Unnamed: 0'].unique().all():
items = ['Unnamed: 0.1','Unnamed: 0']
df.drop(items, axis=1,inplace=True)
df
That being done, obviously there are some null values in the columns, we need to get rid of this null values. But first we need to know the number of null values present in each columns. We do that by calling the df.isnull().sum() function which not only gets the null values in each columns but sums up the total present.
df.isnull().sum()
Well, It seems only the “Rocket” column has null values, 3360 to be exact!. Now we don’t want to loose this much data in the “Rocket” column by using the dropna() function. It is best to replace the null values present with a ‘0.0’ so we know that space contains nothing by using the fillna() function.
df[' Rocket'].fillna('0.0', inplace=True)
df.isnull().sum()
Okay, there are no null values present. So let’s check the actual data set to see how fresh it looks. We call the df.head() function.
df.head()
The ‘NaN’ has been replaced by ‘0.0’ cool!. It is confirmed we have no null values present, Pheeeww!!. Well, the data set looks clean. But we can’t be sure it is. Let’s drop the nail on the head by checking for duplicates, if there is any we have to reduce it to a single form by calling the drop() function.
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
Finally!, It is confirmed we do not have any duplicate. So it is safe to say the data set is clean and is now ready for Data Visualization.
Thanks for taking your time to read!! Follow up to view my article on Data Visualization and Data Analysis.