├── pretty_html_table
├── __init__.py
└── pretty_html_table.py
├── image
├── 1.PNG
├── 2.PNG
├── generate_example_table.py
└── example.html
├── .gitignore
├── setup.py
├── LICENSE.txt
└── README.md
/pretty_html_table/__init__.py:
--------------------------------------------------------------------------------
1 | from .pretty_html_table import build_table
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/image/1.PNG:
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https://raw.githubusercontent.com/sbi-rviot/ph_table/HEAD/image/1.PNG
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/image/2.PNG:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/sbi-rviot/ph_table/HEAD/image/2.PNG
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/.gitignore:
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1 | build/*
2 | dist/*
3 | pretty_html_table.egg-info/*
4 |
5 | #update package
6 | python setup.py sdist bdist_wheel
7 | python -m twine upload -u USERNAME -p PASSWORD dist/*
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/setup.py:
--------------------------------------------------------------------------------
1 | import setuptools
2 |
3 | with open("README.md", "r") as fh:
4 | long_description = fh.read()
5 |
6 |
7 | setuptools.setup(
8 | name = 'pretty_html_table',
9 | version = '0.9.15',
10 | license_files = ('LICENSE.txt',),
11 | author="Renaud Viot",
12 | author_email="renaud.viot@simply-bi.com",
13 | description = 'Make pandas dataframe looking pretty again',
14 | long_description = long_description,
15 | long_description_content_type="text/markdown",
16 | url="https://github.com/sbi-rviot/ph_table",
17 | install_requires = ['pandas'],
18 | packages=['pretty_html_table'],
19 | classifiers=[
20 | "Programming Language :: Python :: 3",
21 | "License :: OSI Approved :: MIT License",
22 | "Operating System :: OS Independent",
23 | ],
24 | )
25 |
26 |
--------------------------------------------------------------------------------
/LICENSE.txt:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) [year] [fullname]
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
--------------------------------------------------------------------------------
/image/generate_example_table.py:
--------------------------------------------------------------------------------
1 | import pandas as pd
2 | from pretty_html_table import build_table
3 |
4 | """
5 | Code which generates example tables html for documentation.
6 | """
7 |
8 | def generate_table():
9 | df = pd.DataFrame(data={
10 | 'ID' : [1,2,3,4],
11 | 'First Name' : ['Flore', 'Grom', 'Truip', 'Ftro'],
12 | 'Last Name' : ['Ju', 'Re', 'Ve', 'Cy'],
13 | 'Age' : [23, 45, 67, 12],
14 | 'Place of Birth' : ['France', 'USA', 'China', 'India'],
15 | 'Date of Birth' : ['1996-10-04', '1974-10-10', '1952-04-07', '2007-10-06']
16 | })
17 |
18 | start = """"""
19 | end = """ """
20 |
21 | output = start \
22 | + 'blue_light
' \
23 | + build_table(
24 | df,
25 | 'blue_light',
26 | width_dict=['10px','700px', '50px', '10px','200px', '50px'],
27 | conditions={
28 | 'Age': {
29 | 'min': 25,
30 | 'max': 60,
31 | 'min_color': 'red',
32 | 'max_color': 'green',
33 | }
34 | }
35 | ) \
36 | + '
blue_dark
' \
37 | + build_table(df, 'blue_dark') \
38 | + '
grey_light
' \
39 | + build_table(df, 'grey_light') \
40 | + '
grey_dark
' \
41 | + build_table(df, 'grey_dark') \
42 | + '
orange_light
' \
43 | + build_table(df, 'orange_light') \
44 | + '
orange_dark
' \
45 | + build_table(df, 'orange_dark') \
46 | + '
yellow_light
' \
47 | + build_table(df, 'yellow_light') \
48 | + '
yellow_dark
' \
49 | + build_table(df, 'yellow_dark') \
50 | + '
green_light
' \
51 | + build_table(df, 'green_light') \
52 | + '
green_dark
' \
53 | + build_table(df, 'green_dark') \
54 | + '
red_light
' \
55 | + build_table(df, 'red_light') \
56 | + '
red_dark
' \
57 | + build_table(df, 'red_dark') \
58 | + end
59 |
60 | with open('example.html', 'w') as f:
61 | f.write(output)
62 |
63 | if __name__ == "__main__":
64 | generate_table()
65 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # pretty_html_table - Beautiful html tables made easy
2 |
3 | `pretty_html_table` exists to convert a pandas DataFrame into a pretty html table for use in email. The intended target audience is anyone who needs to send reports via email and would like to make their tables look more attractive.
4 |
5 | 12 different color themes are available. The output of the package embeds nicely with other packages used to send html emails, such as [email](https://docs.python.org/3/library/email.examples.html) or [O365](https://pypi.org/project/O365/).
6 | The html formatting is set at the DataFrame row level, which allows nearly every email provider to parse it. This obviates the need to grok out how the CSS may interact with the sending/recieving email provider.
7 |
8 | Use [`pip`](https://pypi.org/project/pretty-html-table/) to install the package:
9 |
10 | ```
11 | pip install pretty_html_table
12 | ```
13 |
14 | A simple example to load an Excel file to a pandas DataFrame, convert it to html, and then save to an html file:
15 |
16 | ```
17 | from pretty_html_table import build_table
18 |
19 | df = pd.read_excel('df.xlsx')
20 | html_table_blue_light = build_table(df, 'blue_light')
21 |
22 | # Save to html file
23 | with open('pretty_table.html', 'w') as f:
24 | f.write(html_table_blue_light)
25 |
26 | # Compare to the pandas .to_html method:
27 | with open('pandas_table.html', 'w') as f:
28 | f.write(df.to_html())
29 | ```
30 |
31 | Use any browser to open `pretty_table.html` to see how the table would appear in an html email.
32 |
33 |
34 | ## Why choose pretty_html_table?
35 |
36 | Output is ready to be sent via any Python package used to send emails. Insert the result of this package to the body of the email and voila.
37 |
38 |
39 | ## List of colors available
40 |
41 | | Name | font style | Header | Rows |
42 | |---------------|----------------|---------------------------------------------------------------|-------------------------------------------------------------------|
43 | | 'blue_light' | Century Gothic | Bold: yes / Background color: white / Font color: dark blue | Odd background color: light blue / Even background color: white |
44 | | 'blue_dark' | Century Gothic | Bold: yes / Background color: dark blue / Font color: white | Odd background color: light blue / Even background color: white |
45 | | 'grey_light' | Century Gothic | Bold: yes / Background color: white / Font color: dark grey | Odd background color: light grey / Even background color: white |
46 | | 'grey_dark' | Century Gothic | Bold: yes / Background color: dark grey / Font color: white | Odd background color: light grey / Even background color: white |
47 | | 'orange_light' | Century Gothic | Bold: yes / Background color: white / Font color: dark orange | Odd background color: light orange / Even background color: white |
48 | | 'orange_dark' | Century Gothic | Bold: yes / Background color: dark orange / Font color: white | Odd background color: light orange / Even background color: white |
49 | | 'yellow_light' | Century Gothic | Bold: yes / Background color: white / Font color: dark yellow | Odd background color: light yellow / Even background color: white |
50 | | 'yellow_dark' | Century Gothic | Bold: yes / Background color: dark yellow / Font color: white | Odd background color: light yellow / Even background color: white |
51 | | 'green_light' | Century Gothic | Bold: yes / Background color: white / Font color: dark green | Odd background color: light green / Even background color: white |
52 | | 'green_dark' | Century Gothic | Bold: yes / Background color: dark green / Font color: white | Odd background color: light green / Even background color: white |
53 | | 'red_light' | Century Gothic | Bold: yes / Background color: white / Font color: dark red | Odd background color: light red / Even background color: white |
54 | | 'red_dark' | Century Gothic | Bold: yes / Background color: dark red / Font color: white | Odd background color: light red / Even background color: white |
55 |
56 |
57 | ## Example of an integration with the O365 package
58 |
59 | First, create a function to send an email:
60 |
61 | ```
62 | from O365 import Account
63 |
64 | # Never hard code credentials or store them in a repo
65 | # Use environmental variables instead
66 |
67 | credentials = (o365credid, o365credpwd)
68 | account = Account(credentials)
69 |
70 | def send_email(account, to, subject, start, body, end):
71 | m = account.new_message()
72 | m.to.add(to)
73 | m.subject = subject
74 | m.body = start + body + end
75 | m.send()
76 | ```
77 |
78 | Then create the start and end of an email in html:
79 |
80 | ```
81 | start = """
82 |
83 | Data table here:
"""
84 |
85 |
86 | end = """
87 | """
88 | ```
89 |
90 | Finally we can utilize `pretty_table_html` to convert our Excel file and send the email:
91 |
92 | ```
93 | from pretty_html_table import build_table
94 |
95 | html_table_blue_light = build_table(pd.read_excel('df.xlsx'), 'blue_light')
96 |
97 | send_email(account
98 | , 'test@any.com'
99 | , 'test table'
100 | , start
101 | , html_table_blue_light
102 | , end
103 | )
104 | ```
105 |
106 | Here are all of the currently available colors:
107 |
108 | 
109 | 
110 |
111 | ## Additional arguments
112 | Several optional arguments now exist that allow the user to control the table's font, font size, and alignment:
113 |
114 | * `font_size` - accepts absolute keywords (`medium`) and pixel values (`20px`)
115 | * `font_family` - best practice is to include a generic font family in case a recipient's client cannot render the chosen font. The example below designates `Open Sans` as a font, but designates the generic `sans-serif` family as a fallback. It's possible that the fallback font may be utilized in case a recipient has web fonts blocked for security reasons, or if they are viewing the email on a client that does not have acces to Google Fonts.
116 | * `text_align` - accepts standard html property values such as `left`, `right`, `center`, `justify`.
117 | * `width` - accepts string representation of pixels. For instance, for the columns to have a width of 100px, you would write: width="100px".
118 | * `width_dict` - accepts list of string representation of pixels. It will only work if the length of the list matches the number of columns of your pandas dataframe. You can for example provide the following argument to the fonction: width_dict=['300px','auto', 'auto', 'auto','auto', 'auto'] only the first column would be resized to 300px, the other would be "auto".
119 | * `index` - bolean. False by default - If you write index=True, index of the dataframe will then be visible in your table.
120 | * `even_color` - accepts string representation of colors (either "white" or "FFFFF"). For instance, for the font color of the even lines to be white, you would write: even_color='white'.
121 | * `color` - accepts string representation of colors (either "white" or "FFFFF"). For instance, for the background color of the even lines to be black, you would write: even_color='black'.
122 | * `conditions` - accepts dictionnary providing the following information: : `{'min': ,'max': ,'min_color': ,'max_color': }` Below is an exmaple, if a column name is "Age" and we wish to have the ages represented in red if they are under 25 and green if they are over 60.
123 | * `padding` - accepts a string to set the CSS padding in the table (`10px`, `0px 20px`, `0px 20px 0px 0px`)
124 | * `odd_bg_color` - accepts a hex or standard color for the odd row background
125 | * `border_bottom_color` - accepts a color for the bottom border for the headers
126 |
127 |
128 | ```
129 | html_table = build_table(df
130 | , 'yellow_dark'
131 | , font_size='medium'
132 | , font_family='Open Sans
133 | , sans-serif'
134 | , text_align='left'
135 | , width='auto'
136 | , index=False
137 | ,conditions={
138 | 'Age': {
139 | 'min': 25,
140 | 'max': 60,
141 | 'min_color': 'red',
142 | 'max_color': 'green',
143 | }
144 | }
145 | , even_color='black'
146 | , even_bg_color='white')
147 | ```
148 |
--------------------------------------------------------------------------------
/pretty_html_table/pretty_html_table.py:
--------------------------------------------------------------------------------
1 | import io
2 |
3 | # Reformat table_color as dict of tuples
4 |
5 | dict_colors = {
6 | 'yellow_light' : ('#BF8F00', '2px solid #BF8F00', '#FFF2CC', '#FFFFFF'),
7 | 'grey_light' : ('#808080', '2px solid #808080', '#EDEDED', '#FFFFFF'),
8 | 'blue_light' : ('#305496', '2px solid #305496', '#D9E1F2', '#FFFFFF'),
9 | 'orange_light' : ('#C65911', '2px solid #C65911', '#FCE4D6', '#FFFFFF'),
10 | 'green_light' : ('#548235', '2px solid #548235', '#E2EFDA', '#FFFFFF'),
11 | 'red_light' : ('#823535', '2px solid #823535', '#efdada', '#FFFFFF'),
12 | 'yellow_dark' : ('#FFFFFF', '2px solid #BF8F00', '#FFF2CC', '#BF8F00'),
13 | 'grey_dark' : ('#FFFFFF', '2px solid #808080', '#EDEDED', '#808080'),
14 | 'blue_dark': ('#FFFFFF', '2px solid #305496', '#D9E1F2', '#305496'),
15 | 'orange_dark' : ('#FFFFFF', '2px solid #C65911', '#FCE4D6', '#C65911'),
16 | 'green_dark' : ('#FFFFFF', '2px solid #548235', '#E2EFDA', '#548235'),
17 | 'red_dark' : ('#FFFFFF', '2px solid #823535', '#efdada', '#823535')
18 | }
19 |
20 |
21 | def build_table(
22 | df,
23 | color,
24 | font_size='medium',
25 | font_family='Century Gothic, sans-serif',
26 | text_align='left',
27 | width='auto',
28 | index=False,
29 | even_color='black',
30 | even_bg_color='white',
31 | odd_bg_color=None,
32 | border_bottom_color=None,
33 | escape=True,
34 | width_dict=[],
35 | padding="0px 20px 0px 0px",
36 | float_format=None,
37 | conditions={}):
38 |
39 | if df.empty:
40 | return ''
41 |
42 | # Set color
43 | color, border_bottom, odd_background_color, header_background_color = dict_colors[color]
44 |
45 | if odd_bg_color:
46 | odd_background_color = odd_bg_color
47 |
48 | if border_bottom_color:
49 | border_bottom = border_bottom_color
50 |
51 | a = 0
52 | while a != len(df):
53 | if a == 0:
54 | df_html_output = df.iloc[[a]].to_html(
55 | na_rep="",
56 | index=index,
57 | border=0,
58 | escape=escape,
59 | float_format=float_format,
60 | )
61 | # change format of header
62 | if index:
63 | df_html_output = df_html_output.replace(''
64 | ,' | ', len(df.columns)+1)
72 |
73 | df_html_output = df_html_output.replace(' | '
74 | ,' | ')
80 |
81 | else:
82 | df_html_output = df_html_output.replace(' | '
83 | ,' | ')
91 |
92 | #change format of table
93 | df_html_output = df_html_output.replace(' | '
94 | ,' | ')
100 | body = """ """ + format(df_html_output)
101 |
102 | a = 1
103 |
104 | elif a % 2 == 0:
105 | df_html_output = df.iloc[[a]].to_html(na_rep = "", index = index, header = False, escape=escape)
106 |
107 | # change format of index
108 | df_html_output = df_html_output.replace(' | '
109 | ,' | ')
115 |
116 | #change format of table
117 | df_html_output = df_html_output.replace(' | '
118 | ,' | ')
124 |
125 | body = body + format(df_html_output)
126 |
127 | a += 1
128 |
129 | elif a % 2 != 0:
130 | df_html_output = df.iloc[[a]].to_html(na_rep = "", index = index, header = False, escape=escape)
131 |
132 | # change format of index
133 | df_html_output = df_html_output.replace(' | '
134 | ,' | ')
141 |
142 | #change format of table
143 | df_html_output = df_html_output.replace(' | '
144 | ,' | ')
151 | body = body + format(df_html_output)
152 |
153 | a += 1
154 |
155 | body = body + """"""
156 |
157 | body = body.replace(""" |
158 |
159 |
160 |
161 |
162 |
163 | ""","""
164 |
165 | """).replace("""
166 |
167 |
168 |
169 |
170 | ""","""
171 |
172 | """)
173 |
174 | if conditions:
175 | for k in conditions.keys():
176 | try:
177 | conditions[k]['index'] = list(df.columns).index(k)
178 | width_body = ''
179 | w = 0
180 | for line in io.StringIO(body):
181 | updated_body = False
182 | if w == conditions[k]['index']:
183 | try:
184 | if int(repr(line).split('>')[1].split('<')[0]) < conditions[k]['min']:
185 | if 'color: black' in repr(line):
186 | width_body = width_body + repr(line).replace("color: black", 'color: ' + conditions[k]['min_color'])[1:]
187 | elif 'color: white' in repr(line):
188 | width_body = width_body + repr(line).replace("color: white", 'color: ' + conditions[k]['min_color'])[1:]
189 | else:
190 | width_body = width_body + repr(line).replace('">', '; color: ' + conditions[k]['min_color'] + '">')[1:]
191 | updated_body = True
192 | elif int(repr(line).split('>')[1].split('<')[0]) > conditions[k]['max']:
193 | if 'color: black' in repr(line):
194 | width_body = width_body + repr(line).replace("color: black", 'color: ' + conditions[k]['max_color'])[1:]
195 | elif 'color: white' in repr(line):
196 | width_body = width_body + repr(line).replace("color: white", 'color: ' + conditions[k]['max_color'])[1:]
197 | else:
198 | width_body = width_body + repr(line).replace('">', '; color: ' + conditions[k]['max_color'] + '">')[1:]
199 | updated_body = True
200 | except:
201 | pass
202 | if not updated_body:
203 | width_body = width_body + repr(line)[1:]
204 |
205 | if str(repr(line))[:10] == "' blue_light
| ID | First Name | Last Name | Age | Place of Birth | Date of Birth |
| 1 | Flore | Ju | 23 | France | 1996-10-04 |
| 2 | Grom | Re | 45 | USA | 1974-10-10 |
| 3 | Truip | Ve | 67 | China | 1952-04-07 |
| 4 | Ftro | Cy | 12 | India | 2007-10-06 |
blue_dark
2 |
3 |
4 | | ID |
5 | First Name |
6 | Last Name |
7 | Age |
8 | Place of Birth |
9 | Date of Birth |
10 |
11 |
12 |
13 |
14 | | 1 |
15 | Flore |
16 | Ju |
17 | 23 |
18 | France |
19 | 1996-10-04 |
20 |
21 |
22 | | 2 |
23 | Grom |
24 | Re |
25 | 45 |
26 | USA |
27 | 1974-10-10 |
28 |
29 |
30 | | 3 |
31 | Truip |
32 | Ve |
33 | 67 |
34 | China |
35 | 1952-04-07 |
36 |
37 |
38 | | 4 |
39 | Ftro |
40 | Cy |
41 | 12 |
42 | India |
43 | 2007-10-06 |
44 |
45 |
46 |
grey_light
47 |
48 |
49 | | ID |
50 | First Name |
51 | Last Name |
52 | Age |
53 | Place of Birth |
54 | Date of Birth |
55 |
56 |
57 |
58 |
59 | | 1 |
60 | Flore |
61 | Ju |
62 | 23 |
63 | France |
64 | 1996-10-04 |
65 |
66 |
67 | | 2 |
68 | Grom |
69 | Re |
70 | 45 |
71 | USA |
72 | 1974-10-10 |
73 |
74 |
75 | | 3 |
76 | Truip |
77 | Ve |
78 | 67 |
79 | China |
80 | 1952-04-07 |
81 |
82 |
83 | | 4 |
84 | Ftro |
85 | Cy |
86 | 12 |
87 | India |
88 | 2007-10-06 |
89 |
90 |
91 |
grey_dark
92 |
93 |
94 | | ID |
95 | First Name |
96 | Last Name |
97 | Age |
98 | Place of Birth |
99 | Date of Birth |
100 |
101 |
102 |
103 |
104 | | 1 |
105 | Flore |
106 | Ju |
107 | 23 |
108 | France |
109 | 1996-10-04 |
110 |
111 |
112 | | 2 |
113 | Grom |
114 | Re |
115 | 45 |
116 | USA |
117 | 1974-10-10 |
118 |
119 |
120 | | 3 |
121 | Truip |
122 | Ve |
123 | 67 |
124 | China |
125 | 1952-04-07 |
126 |
127 |
128 | | 4 |
129 | Ftro |
130 | Cy |
131 | 12 |
132 | India |
133 | 2007-10-06 |
134 |
135 |
136 |
orange_light
137 |
138 |
139 | | ID |
140 | First Name |
141 | Last Name |
142 | Age |
143 | Place of Birth |
144 | Date of Birth |
145 |
146 |
147 |
148 |
149 | | 1 |
150 | Flore |
151 | Ju |
152 | 23 |
153 | France |
154 | 1996-10-04 |
155 |
156 |
157 | | 2 |
158 | Grom |
159 | Re |
160 | 45 |
161 | USA |
162 | 1974-10-10 |
163 |
164 |
165 | | 3 |
166 | Truip |
167 | Ve |
168 | 67 |
169 | China |
170 | 1952-04-07 |
171 |
172 |
173 | | 4 |
174 | Ftro |
175 | Cy |
176 | 12 |
177 | India |
178 | 2007-10-06 |
179 |
180 |
181 |
orange_dark
182 |
183 |
184 | | ID |
185 | First Name |
186 | Last Name |
187 | Age |
188 | Place of Birth |
189 | Date of Birth |
190 |
191 |
192 |
193 |
194 | | 1 |
195 | Flore |
196 | Ju |
197 | 23 |
198 | France |
199 | 1996-10-04 |
200 |
201 |
202 | | 2 |
203 | Grom |
204 | Re |
205 | 45 |
206 | USA |
207 | 1974-10-10 |
208 |
209 |
210 | | 3 |
211 | Truip |
212 | Ve |
213 | 67 |
214 | China |
215 | 1952-04-07 |
216 |
217 |
218 | | 4 |
219 | Ftro |
220 | Cy |
221 | 12 |
222 | India |
223 | 2007-10-06 |
224 |
225 |
226 |
yellow_light
227 |
228 |
229 | | ID |
230 | First Name |
231 | Last Name |
232 | Age |
233 | Place of Birth |
234 | Date of Birth |
235 |
236 |
237 |
238 |
239 | | 1 |
240 | Flore |
241 | Ju |
242 | 23 |
243 | France |
244 | 1996-10-04 |
245 |
246 |
247 | | 2 |
248 | Grom |
249 | Re |
250 | 45 |
251 | USA |
252 | 1974-10-10 |
253 |
254 |
255 | | 3 |
256 | Truip |
257 | Ve |
258 | 67 |
259 | China |
260 | 1952-04-07 |
261 |
262 |
263 | | 4 |
264 | Ftro |
265 | Cy |
266 | 12 |
267 | India |
268 | 2007-10-06 |
269 |
270 |
271 |
yellow_dark
272 |
273 |
274 | | ID |
275 | First Name |
276 | Last Name |
277 | Age |
278 | Place of Birth |
279 | Date of Birth |
280 |
281 |
282 |
283 |
284 | | 1 |
285 | Flore |
286 | Ju |
287 | 23 |
288 | France |
289 | 1996-10-04 |
290 |
291 |
292 | | 2 |
293 | Grom |
294 | Re |
295 | 45 |
296 | USA |
297 | 1974-10-10 |
298 |
299 |
300 | | 3 |
301 | Truip |
302 | Ve |
303 | 67 |
304 | China |
305 | 1952-04-07 |
306 |
307 |
308 | | 4 |
309 | Ftro |
310 | Cy |
311 | 12 |
312 | India |
313 | 2007-10-06 |
314 |
315 |
316 |
green_light
317 |
318 |
319 | | ID |
320 | First Name |
321 | Last Name |
322 | Age |
323 | Place of Birth |
324 | Date of Birth |
325 |
326 |
327 |
328 |
329 | | 1 |
330 | Flore |
331 | Ju |
332 | 23 |
333 | France |
334 | 1996-10-04 |
335 |
336 |
337 | | 2 |
338 | Grom |
339 | Re |
340 | 45 |
341 | USA |
342 | 1974-10-10 |
343 |
344 |
345 | | 3 |
346 | Truip |
347 | Ve |
348 | 67 |
349 | China |
350 | 1952-04-07 |
351 |
352 |
353 | | 4 |
354 | Ftro |
355 | Cy |
356 | 12 |
357 | India |
358 | 2007-10-06 |
359 |
360 |
361 |
green_dark
362 |
363 |
364 | | ID |
365 | First Name |
366 | Last Name |
367 | Age |
368 | Place of Birth |
369 | Date of Birth |
370 |
371 |
372 |
373 |
374 | | 1 |
375 | Flore |
376 | Ju |
377 | 23 |
378 | France |
379 | 1996-10-04 |
380 |
381 |
382 | | 2 |
383 | Grom |
384 | Re |
385 | 45 |
386 | USA |
387 | 1974-10-10 |
388 |
389 |
390 | | 3 |
391 | Truip |
392 | Ve |
393 | 67 |
394 | China |
395 | 1952-04-07 |
396 |
397 |
398 | | 4 |
399 | Ftro |
400 | Cy |
401 | 12 |
402 | India |
403 | 2007-10-06 |
404 |
405 |
406 |
red_light
407 |
408 |
409 | | ID |
410 | First Name |
411 | Last Name |
412 | Age |
413 | Place of Birth |
414 | Date of Birth |
415 |
416 |
417 |
418 |
419 | | 1 |
420 | Flore |
421 | Ju |
422 | 23 |
423 | France |
424 | 1996-10-04 |
425 |
426 |
427 | | 2 |
428 | Grom |
429 | Re |
430 | 45 |
431 | USA |
432 | 1974-10-10 |
433 |
434 |
435 | | 3 |
436 | Truip |
437 | Ve |
438 | 67 |
439 | China |
440 | 1952-04-07 |
441 |
442 |
443 | | 4 |
444 | Ftro |
445 | Cy |
446 | 12 |
447 | India |
448 | 2007-10-06 |
449 |
450 |
451 |
red_dark
452 |
453 |
454 | | ID |
455 | First Name |
456 | Last Name |
457 | Age |
458 | Place of Birth |
459 | Date of Birth |
460 |
461 |
462 |
463 |
464 | | 1 |
465 | Flore |
466 | Ju |
467 | 23 |
468 | France |
469 | 1996-10-04 |
470 |
471 |
472 | | 2 |
473 | Grom |
474 | Re |
475 | 45 |
476 | USA |
477 | 1974-10-10 |
478 |
479 |
480 | | 3 |
481 | Truip |
482 | Ve |
483 | 67 |
484 | China |
485 | 1952-04-07 |
486 |
487 |
488 | | 4 |
489 | Ftro |
490 | Cy |
491 | 12 |
492 | India |
493 | 2007-10-06 |
494 |
495 |
496 |