-
Notifications
You must be signed in to change notification settings - Fork 0
/
myportfolio.html
182 lines (154 loc) · 4.99 KB
/
myportfolio.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
<!DOCTYPE html>
<html lang="en">
<div id="medium_blog_container">
<!-- Here we will mount our component -->
</div>
<body>
<script src="https://unpkg.com/react@18/umd/react.development.js" crossorigin></script>
<script src="https://unpkg.com/react-dom@18/umd/react-dom.development.js" crossorigin></script>
</body>
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Data Manipulation Portfolio</title>
<style>
/* Add your custom CSS styles here */
body {
font-family: Arial, sans-serif;
line-height: 1.6;
margin: 0;
padding: 0;
padding-left: 20px;
background-image: url('picture3.jpg') ;/*('pagePic.jpg')*/
background-size: cover;
}
#container {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
min-height: 100vh;
margin-left: 20px;
text-align: center;
}
/* Additional CSS for your content */
/* For example, you can style the header text */
header {
font-size: 20px;
}
p{
/*color:rgb(0, 26, 255)*/
color: black /*whitesmoke*/
}
h1{
color:black /*red*/
}
h2{
color:blanchedalmond/*red*/
}
/* ... Additional CSS styling ... */
</style>
</head>
<body>
<header>
<h1>Data Manipulation Portfolio</h1>
<!-- Add your personal information like name, email, and LinkedIn profile here -->
<address>
Email: ambrolum@gmail.com<br>
GitHub: <a href="https://github.com/Ambro19">github.com/Ambro19</a>
</address>
</header>
<section>
<h2>Select Projects</h2>
<!-- Add a brief description of your selected LabVIEW projects -->
<p>Pandas is a powerful open-source library for data manipulation and analysis in Python. It provides data structures and functions that make
it easy to work with structured data, such as tables and time series, and perform various data operations efficiently.</p>
</section>
<section>
<h2>Project Descriptions</h2>
<!-- Write detailed descriptions for each project -->
<p> Project 1: Data Manipulation System<br>
Goals:
<ul>
<li>Manipulate datasets using various operations and processes.</li>
</ul>
Challenges:
<ul>
<li>Find a suitable framework to meet the project's expectations.</li>
</ul>
Solutions:
<ul>
<li>Develop Python code for tasks such as DataFrame manipulation, data filtering, sorting, and more.</li>
</ul>
</p>
<!-- Add more project descriptions as needed -->
</section>
<section>
<h2>Code Samples</h2>
<!-- Share snippets of your LabVIEW code -->
<pre>
<code>
# Create a DataFrame
data = {'Name': ['John', 'Emma', 'Michael', 'Sophia', 'William'],
'Age': [25, 28, 31, np.nan, 22],
'Gender': ['M', 'F', 'M', 'F', 'M'],
'Salary': [50000, 60000, np.nan, 55000, 45000]}
df = pd.DataFrame(data)
# Data manipulation
print("Original DataFrame:")
print(df)
# Selecting specific columns
selected_columns = df[['Name', 'Age']]
print("\nSelected columns:")
print(selected_columns)
# Filtering data
filtered_data = df[df['Salary'] > 50000]
print("\nFiltered data:")
print(filtered_data)
# Sorting data
sorted_data = df.sort_values('Age', ascending=False)
print("\nSorted data:")
print(sorted_data)
# Data cleaning
# Handling missing values
df['Age'].fillna(df['Age'].mean(), inplace=True)
print("\nDataFrame with filled missing values:")
print(df)
# Removing duplicates
df.drop_duplicates(subset=['Name'], inplace=True)
print("\nDataFrame without duplicates:")
print(df)
# Data analysis
# Computing descriptive statistics
statistics = df.describe()
print("\nDescriptive statistics:")
print(statistics)
# Calculating correlations
correlation = df.corr()
print("\nCorrelation matrix:")
print(correlation)
# Time series analysis
# Creating a time series index
date_range = pd.date_range('2023-01-01', periods=len(df), freq='M')
df['Date'] = date_range
# Shifting values
df['Previous Salary'] = df['Salary'].shift(1)
print("\nDataFrame with shifted values:")
print(df)
# Input and output
# Writing data to a CSV file
df.to_csv('data.csv', index=False)
# Reading data from a CSV file
df_read = pd.read_csv('data.csv')
print("\nDataFrame read from CSV:")
print(df_read)
</code>
</pre>
</section>
<!-- Add more sections for Screenshots and Media, Documentation, Learning Journey, Collaborative Projects,
Personal Projects, Online Presence, Presentation, and Keep It Updated. -->
<footer>
<p>© 2023 Ambroise Ngayinoko. All rights reserved.</p>
</footer>
</body>
</html>