1586510625
Using the Element.closest() method
In today’s video I’ll be demonstrating the usage of the Element.closest() method in JavaScript - this can be used to traverse up the tree until an element is found with the provided selector.
#js #javascript
What is GEEK
Buddha Community
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Pdf2gerb: Perl Script Converts PDF Files to Gerber format
pdf2gerb
Perl script converts PDF files to Gerber format
Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.
The general workflow is as follows:
- Design the PCB using your favorite CAD or drawing software.
- Print the top and bottom copper and top silk screen layers to a PDF file.
- Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
- Use a Gerber viewer to double-check the output against the original PCB design.
- Make adjustments as needed.
- Submit the files to a PCB manufacturer.
Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).
See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.
pdf2gerb_cfg.pm
#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;
use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)
##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file
use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call
#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software. \nGerber files MAY CONTAIN ERRORS. Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG
use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC
use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)
#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1);
#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
.010, -.001, #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
.031, -.014, #used for vias
.041, -.020, #smallest non-filled plated hole
.051, -.025,
.056, -.029, #useful for IC pins
.070, -.033,
.075, -.040, #heavier leads
# .090, -.043, #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
.100, -.046,
.115, -.052,
.130, -.061,
.140, -.067,
.150, -.079,
.175, -.088,
.190, -.093,
.200, -.100,
.220, -.110,
.160, -.125, #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
.090, -.040, #want a .090 pad option, but use dummy hole size
.065, -.040, #.065 x .065 rect pad
.035, -.040, #.035 x .065 rect pad
#traces:
.001, #too thin for real traces; use only for board outlines
.006, #minimum real trace width; mainly used for text
.008, #mainly used for mid-sized text, not traces
.010, #minimum recommended trace width for low-current signals
.012,
.015, #moderate low-voltage current
.020, #heavier trace for power, ground (even if a lighter one is adequate)
.025,
.030, #heavy-current traces; be careful with these ones!
.040,
.050,
.060,
.080,
.100,
.120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);
#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size: parsed PDF diameter: error:
# .014 .016 +.002
# .020 .02267 +.00267
# .025 .026 +.001
# .029 .03167 +.00267
# .033 .036 +.003
# .040 .04267 +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};
#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
CIRCLE_ADJUST_MINX => 0,
CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
CIRCLE_ADJUST_MAXY => 0,
SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};
#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches
#line join/cap styles:
use constant
{
CAP_NONE => 0, #butt (none); line is exact length
CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
#number of elements in each shape type:
use constant
{
RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
rect => RECT_SHAPELEN,
line => LINE_SHAPELEN,
curve => CURVE_SHAPELEN,
circle => CIRCLE_SHAPELEN,
);
#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions
# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?
#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes.
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes
#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches
# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)
# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time
# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const
use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool
my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time
print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load
#############################################################################################
#junk/experiment:
#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html
#my $caller = "pdf2gerb::";
#sub cfg
#{
# my $proto = shift;
# my $class = ref($proto) || $proto;
# my $settings =
# {
# $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
# };
# bless($settings, $class);
# return $settings;
#}
#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;
#print STDERR "read cfg file\n";
#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names
#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }Download Details:
Author: swannman
Source Code: https://github.com/swannman/pdf2gerb
License: GPL-3.0 license
1620729846
Why Use WordPress? What Can You Do With WordPress?
Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?
WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:
1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.
2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.
3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.
4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.
5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.
6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.
#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website
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輕鬆過濾 Pandas 數據框的 14 種方法
每當我們處理任何類型的數據時，我們都需要清楚地了解我們正在處理的數據類型。對於那裡的大多數數據，其中可能包含數千甚至數百萬個包含各種信息的條目，如果沒有任何工具以簡短易讀的格式呈現數據，就真的不可能理解這些數據。
大多數時候，我們需要瀏覽數據、操作數據並將其可視化以獲得洞察力。嗯，有一個很棒的庫，它的名字叫 pandas，它為我們提供了這種能力。最常見的數據操作操作是數據過濾。它與 SQL 中的 WHERE 子句非常相似，或者您必須在 MS Excel 中使用過濾器來根據某些條件選擇特定行。
pandas是一個強大、靈活和開源的數據分析/操作工具，它本質上是一個python 包，提供速度、靈活性和富有表現力的數據結構，以直觀和簡單的方式處理關係”或“標記它是在 Python 中執行實際數據分析的最流行的庫
pandas建立在 NumPy 庫之上，旨在與科學計算環境和眾多其他第三方庫很好地集成。它有兩個主要數據結構，即Series (1D)和Dataframes(2D)，在大多數實際用例中，這是金融、科學計算、工程和統計等許多領域正在處理的數據類型。
讓我們開始在 Pandas Dataframe 的幫助下過濾數據
安裝熊貓
!pip install pandas導入 Pandas 庫，讀取我們的示例數據文件並將其分配給“df” DataFrame
import pandas as pd
df = pd.read_csv(r"C:\Users\rajam\Desktop\sample_data.csv")讓我們看看我們的數據框：
print(df.head())
樣本數據
現在我們有了 DataFrame，我們將應用各種方法來過濾它。
方法 - 1：按列值過濾 DataFrame
我們的 DataFrame 中有一個名為“Total_Sales”的列，我們想要過濾掉所有大於 300 的銷售額。
#Filter a DataFrame for a single column value with a given condition
greater_than = df[df['Total_Sales'] > 300]
print(greater_than.head())
銷售額超過 300
Method – 2 : Filtering DataFrame based on multiple conditions
在這裡，我們過濾“Total_Sales”值大於 300 以及“Units”大於 20 的所有值。我們將不得不使用執行按位與操作的 python 運算符“&”以顯示相應的結果。
#Filter a DataFrame with multiple conditions
filter_sales_units = df[(df['Total_Sales'] > 300) & (df["Units"] > 20)]
print(Filter_sales_units.head())
篩選銷售額和單位
方法 – 3：根據日期值過濾 DataFrame
如果我們想根據某個日期值過濾我們的數據框，例如這裡我們試圖獲取基於特定日期的所有結果，在我們的例子中是日期 '03/10/21' 之後的結果。
#Filter a DataFrame based on specific date
date_filter = df[df['Date'] > '03/10/21']
print(date_filter.head())
按日期過濾
方法四：基於Date值多條件過濾DataFrame
在這裡，我們得到了評估多個日期的 Date 操作的所有結果。
#Filter a DataFrame with multiple conditions our Date value
date_filter2 = df[(df['Date'] >= '3/25/2021') & (df['Date'] <'8/17/2021')]
print(date_filter2.head())
篩選具有多個條件的日期
方法五：根據特定字符串過濾DataFrame
在這裡，我們選擇一個名為“Region”的列並獲取來自“East”區域的所有行，從而根據特定的字符串值進行過濾。
#Filter a DataFrame to a specific string
east = df[df['Region'] == 'East']
print(east.head())
根據特定字符串過濾
方法6：根據字符串中的特定索引值過濾 DataFrame
在這裡，我們選擇一個名為“Region”的列，並獲取所有以字母“E”作為第一個字符的行，即指定列結果中索引 0 處的所有行。
#Filter a DataFrame to show rows starting with a specfic letter
starting_with_e = df[df['Region'].str[0]== 'E']
print(starting_with_e.head())
根據特定字母過濾
方法7：根據值列表過濾 DataFrame
在這裡，我們過濾包含值“West”和“East”的“Region”列中的行，並顯示組合結果。可以使用兩種方法來執行此過濾，即使用管道 | 具有相應所需值集的運算符具有以下語法，或者我們可以使用.isin()函數過濾給定列中的值，在我們的例子中是“區域”，並提供所需集的列表它裡面的值作為一個列表。
#Filter a DataFrame rows based on list of values
#Method 1:
east_west = df[(df['Region'] == 'West') | (df['Region'] == 'East')]
print(east_west)
#Method 2:
east_west_1 = df[df['Region'].isin(['West', 'East'])]
print(east_west_1.head())
方法-2的輸出
方法 – 8：使用 RegEx 根據特定值過濾 DataFrame 行
在這裡，我們想要列 'Region' 中的所有值，並在其字符串值中以 'th'結尾並顯示它們。換句話說，我們希望我們的結果顯示 'Nor th ' 和 'Sout th ' 的值並忽略 'East' 和 'West'。具有指定值的方法.str.contains()以及$ RegEx 模式可用於獲得所需的結果。
有關更多信息，請查看正則表達式文檔
#Filtering the DataFrame rows using regular expressions(REGEX)
regex_df = df[df['Region'].str.contains('th$')]
print(regex_df.head())
基於 REGEX 的過濾器
方法9：過濾 DataFrame 以檢查null
在這裡，我們將在isnull() 函數的幫助下檢查所有列中的空值和非空值。
#Filtering to check for null and not null values in all columns
df_null = df[df.isnull().any(axis=1)]
print(df_null.head())
基於 NULL 或 NOT 空值過濾
方法 - 10：過濾 DataFrame 以檢查特定列中的空值。
#Filtering to check for null values if any in the 'Units' column
units_df = df[df['Units'].isnull()]
print(units_df.head())
在特定列上查找空值
方法 – 11：過濾 DataFrame 以檢查特定列中的非 空值
#Filtering to check for not null values in the 'Units' column
df_not_null = df[df['Units'].notnull()]
print(df_not_null.head())
在特定列上查找非空值
Method – 12: Filtering DataFrame using query()with a condition
#Using query function in pandas
df_query = df.query('Total_Sales > 300')
print(df_query.head())
Query使用函數過濾值
Method – 13: Filtering DataFrame using query()with multiple conditions
#Using query function with multiple conditions in pandas
df_query_1 = df.query('Total_Sales > 300 and Units <18')
print(df_query_1.head())
Query使用函數過濾多列
方法 –loc 14：使用和iloc函數過濾我們的 DataFrame 。
#Creating a sample DataFrame for illustrations
import numpy as np
data = pd.DataFrame({"col1" : np.arange(1, 20 ,2)}, index=[19, 18 ,8, 6, 0, 1, 2, 3, 4, 5])
print(data)
樣本數據
解釋：iloc 根據給定索引的位置考慮行，因此它僅將整數作為值。
有關更多信息，請查看Pandas 文檔
#Filter with iloc
data.iloc[0 : 5]
過濾使用iloc
說明：loc 考慮基於索引標籤的行
#Filter with loc
data.loc[0 : 5]
過濾使用loc
您可能正在思考為什麼loc函數返回 6 行而不是 5 行。這是因為不會根據索引位置產生輸出。它只考慮索引標籤，它也可以是字母表，包括起點和終點。 loc
結論
因此，這些是 pandas 中最常用的一些過濾方法。還有許多其他過濾方法可以使用，但這些是最常見的一些。
鏈接：https ://www.askpython.com/python-modules/pandas/filter-pandas-dataframe
#pandas #python #datafame
1660017761
14 Meilleures Façons De Filtrer Facilement Les Dataframes Pandas
Chaque fois que nous travaillons avec des données de toutes sortes, nous avons besoin d'une image claire du type de données avec lesquelles nous traitons. Pour la plupart des données disponibles, qui peuvent contenir des milliers, voire des millions d'entrées avec une grande variété d'informations, il est vraiment impossible de donner un sens à ces données sans aucun outil pour présenter les données dans un format court et lisible.
La plupart du temps, nous devons parcourir les données, les manipuler et les visualiser pour obtenir des informations. Eh bien, il existe une excellente bibliothèque qui porte le nom de pandas et qui nous offre cette capacité. L'opération de manipulation de données la plus fréquente est le filtrage de données. Il est très similaire à la clause WHERE dans SQL ou vous devez avoir utilisé un filtre dans MS Excel pour sélectionner des lignes spécifiques en fonction de certaines conditions.
pandas est un outil d'analyse/manipulation de données puissant, flexible et open source qui est essentiellement unpackage pythonqui offre vitesse, flexibilité et structures de données expressives conçues pour fonctionner avec des données « relationnelles » ou « étiquetées » de manière intuitive et simple. C'est l'une des bibliothèques les plus populairespour effectuer une analyse de données du monde réel en Python.
pandas est construit au-dessus de la bibliothèque NumPy qui vise à bien s'intégrer à l'environnement informatique scientifique et à de nombreuses autres bibliothèques tierces. Il comporte deux structures de données principales, à savoir Series (1D) et Dataframes (2D) , qui, dans la plupart des cas d'utilisation réels, correspondent au type de données traitées dans de nombreux secteurs de la finance, du calcul scientifique, de l'ingénierie et des statistiques.
Commençons à filtrer les données à l'aide de Pandas Dataframe
Installer des pandas
!pip install pandasImportation de la bibliothèque Pandas, lecture de notre exemple de fichier de données et affectation à "df" DataFrame
import pandas as pd
df = pd.read_csv(r"C:\Users\rajam\Desktop\sample_data.csv")Voyons notre dataframe :
print(df.head())
Sample_data
Maintenant que nous avons notre DataFrame, nous allons appliquer différentes méthodes pour le filtrer.
Méthode – 1 : Filtrage de DataFrame par valeur de colonne
Nous avons une colonne nommée "Total_Sales" dans notre DataFrame et nous voulons filtrer toute la valeur des ventes supérieure à 300.
#Filter a DataFrame for a single column value with a given condition
greater_than = df[df['Total_Sales'] > 300]
print(greater_than.head())
Ventes avec plus de 300
Méthode – 2 : Filtrage de DataFrame basé sur plusieurs conditions
Ici, nous filtrons toutes les valeurs dont la valeur "Total_Sales" est supérieure à 300 et également où les "Unités" sont supérieures à 20. Nous devrons utiliser l'opérateur python "&" qui effectue une opération ET au niveau du bit afin d'afficher le résultat correspondant.
#Filter a DataFrame with multiple conditions
filter_sales_units = df[(df['Total_Sales'] > 300) & (df["Units"] > 20)]
print(Filter_sales_units.head())
Filtrer sur les ventes et les unités
Méthode - 3 : Filtrage de DataFrame basé sur la valeur Date
Si nous voulons filtrer notre trame de données en fonction d'une certaine valeur de date, par exemple ici nous essayons d'obtenir tous les résultats en fonction d'une date particulière, dans notre cas les résultats après la date '03/10/21'.
#Filter a DataFrame based on specific date
date_filter = df[df['Date'] > '03/10/21']
print(date_filter.head())
Filtrer par date
Méthode - 4 : Filtrage de DataFrame en fonction de la valeur Date avec plusieurs conditions
Ici, nous obtenons tous les résultats de notre opération Date évaluant plusieurs dates .
#Filter a DataFrame with multiple conditions our Date value
date_filter2 = df[(df['Date'] >= '3/25/2021') & (df['Date'] <'8/17/2021')]
print(date_filter2.head())
Filtrer sur une date avec plusieurs conditions
Méthode - 5 : Filtrage de DataFrame en fonction d'une chaîne spécifique
Ici, nous sélectionnons une colonne appelée 'Region' et obtenons toutes les lignes qui proviennent de la région 'East', filtrant ainsi en fonction d'une valeur de chaîne spécifique .
#Filter a DataFrame to a specific string
east = df[df['Region'] == 'East']
print(east.head())
Filtre basé sur une chaîne spécifique
Méthode - 6 : Filtrage de DataFrame en fonction d'une valeur d'index spécifique dans une chaîne
Ici, nous sélectionnons une colonne appelée 'Region' et obtenons toutes les lignes qui ont la lettre 'E' comme premier caractère, c'est-à-dire à l'index 0 dans les résultats de colonne spécifiés.
#Filter a DataFrame to show rows starting with a specfic letter
starting_with_e = df[df['Region'].str[0]== 'E']
print(starting_with_e.head())
Filtre basé sur une lettre spécifique
Méthode - 7 : Filtrage de DataFrame basé sur une liste de valeurs
Ici, nous filtrons les lignes dans la colonne « Région » qui contient les valeurs « Ouest » ainsi que « Est » et affichons le résultat combiné. Deux méthodes peuvent être utilisées pour effectuer ce filtrage à savoir l'utilisation d'un tube | opérateur avec l'ensemble de valeurs souhaité correspondant avec la syntaxe ci-dessous OU nous pouvons utiliser la fonction .isin() pour filtrer les valeurs dans une colonne donnée, qui dans notre cas est la 'Région', et fournir la liste de l'ensemble souhaité de valeurs à l'intérieur sous forme de liste.
#Filter a DataFrame rows based on list of values
#Method 1:
east_west = df[(df['Region'] == 'West') | (df['Region'] == 'East')]
print(east_west)
#Method 2:
east_west_1 = df[df['Region'].isin(['West', 'East'])]
print(east_west_1.head())
Sortie de la méthode -2
Méthode - 8: Filtrage des lignes DataFrame en fonction de valeurs spécifiques à l'aide de RegEx
Ici, nous voulons toutes les valeurs de la colonne 'Region' , qui se termine par 'th' dans leur valeur de chaîne et les afficher. En d'autres termes, nous voulons que nos résultats montrent les valeurs de « Nord » et « Sud » et ignorent « Est » et « Ouest » . La méthode .str.contains() avec les valeurs spécifiées avec le modèle $ RegEx peut être utilisée pour obtenir les résultats souhaités.
Pour plus d'informations, veuillez consulter la documentation Regex
#Filtering the DataFrame rows using regular expressions(REGEX)
regex_df = df[df['Region'].str.contains('th$')]
print(regex_df.head())
Filtre basé sur REGEX
Méthode - 9: Filtrage de DataFrame pour vérifier null
Ici, nous allons vérifier les valeurs nulles et non nulles dans toutes les colonnes à l'aide de la fonction isnull() .
#Filtering to check for null and not null values in all columns
df_null = df[df.isnull().any(axis=1)]
print(df_null.head())
Filtre basé sur les valeurs NULL ou NOT null
Méthode - 10 : Filtrage de DataFrame pour vérifier les valeurs nulles dans une colonne spécifique.
#Filtering to check for null values if any in the 'Units' column
units_df = df[df['Units'].isnull()]
print(units_df.head())
Recherche de valeurs nulles sur des colonnes spécifiques
Méthode - 11 : Filtrage de DataFrame pour vérifier les valeurs non nulles dans des colonnes spécifiques
#Filtering to check for not null values in the 'Units' column
df_not_null = df[df['Units'].notnull()]
print(df_not_null.head())
Recherche de valeurs non nulles sur des colonnes spécifiques
Méthode - 12: Filtrage de DataFrame à l'aide query()d'une condition
#Using query function in pandas
df_query = df.query('Total_Sales > 300')
print(df_query.head())
Filtrer les valeurs avec Queryla fonction
Méthode - 13: Filtrage de DataFrame à l'aide query()de plusieurs conditions
#Using query function with multiple conditions in pandas
df_query_1 = df.query('Total_Sales > 300 and Units <18')
print(df_query_1.head())
Filtrer plusieurs colonnes avec QueryFunction
Méthode – 14 : Filtrage de notre DataFrame à l'aide des fonctions locet iloc.
#Creating a sample DataFrame for illustrations
import numpy as np
data = pd.DataFrame({"col1" : np.arange(1, 20 ,2)}, index=[19, 18 ,8, 6, 0, 1, 2, 3, 4, 5])
print(data)
sample_data
Explication : iloc considère les lignes en fonction de la position de l'index donné, de sorte qu'il ne prend que des entiers comme valeurs.
Pour plus d'informations, veuillez consulter la documentation de Pandas
#Filter with iloc
data.iloc[0 : 5]
Filtrer en utilisantiloc
Explication : loc considère les lignes en fonction des étiquettes d'index
#Filter with loc
data.loc[0 : 5]
Filtrer en utilisantloc
Vous vous demandez peut-être pourquoi la locfonction renvoie 6 lignes au lieu de 5 lignes. En effet , ne produit pas de sortie basée sur la position de l'index. Il ne prend en compte que les étiquettes d'index qui peuvent également être un alphabet et incluent à la fois le point de départ et le point final. loc
Conclusion
Donc, ce sont quelques-unes des méthodes de filtrage les plus couramment utilisées dans les pandas. Il existe de nombreuses autres méthodes de filtrage qui pourraient être utilisées, mais celles-ci sont parmi les plus courantes.
Lien : https://www.askpython.com/python-modules/pandas/filter-pandas-dataframe
#pandas #python #datafame
1660147320
Top 14 Ways to Filter Pandas Dataframes Easily
Whenever we work with data of any sort, we need a clear picture of the kind of data that we are dealing with. For most of the data out there, which may contain thousands or even millions of entries with a wide variety of information, it’s really impossible to make sense of that data without any tool to present the data in a short and readable format.
Most of the time we need to go through the data, manipulate it, and visualize it for getting insights. Well, there is a great library which goes by the name pandas which provides us with that capability. The most frequent Data manipulation operation is Data Filtering. It is very similar to the WHERE clause in SQL or you must have used a filter in MS Excel for selecting specific rows based on some conditions.
pandas is a powerful, flexible and open source data analysis/manipulation tool which is essentially a python package that provides speed, flexibility and expressive data structures crafted to work with “relational” or “labelled” data in an intuitive and easy manner. It is one of the most popular libraries to perform real-world data analysis in Python.
pandas is built on top of the NumPy library which aims to integrate well with the scientific computing environment and numerous other 3rd party libraries. It has two primary data structures namely Series (1D) and Dataframes(2D), which in most real-world use cases is the type of data that is being dealt with in many sectors of finance, scientific computing, engineering and statistics.
Let’s Start Filtering Data With the Help of Pandas Dataframe
Installing pandas
!pip install pandasImporting the Pandas library, reading our sample data file and assigning it to “df” DataFrame
import pandas as pd
df = pd.read_csv(r"C:\Users\rajam\Desktop\sample_data.csv")Let’s check out our dataframe:
print(df.head())
Sample_data
Now that we have our DataFrame, we will be applying various methods to filter it.
Method – 1: Filtering DataFrame by column value
We have a column named “Total_Sales” in our DataFrame and we want to filter out all the sales value which is greater than 300.
#Filter a DataFrame for a single column value with a given condition
greater_than = df[df['Total_Sales'] > 300]
print(greater_than.head())
Sales with Greater than 300
Method – 2: Filtering DataFrame based on multiple conditions
Here we are filtering all the values whose “Total_Sales” value is greater than 300 and also where the “Units” is greater than 20. We will have to use the python operator “&” which performs a bitwise AND operation in order to display the corresponding result.
#Filter a DataFrame with multiple conditions
filter_sales_units = df[(df['Total_Sales'] > 300) & (df["Units"] > 20)]
print(Filter_sales_units.head())
Filter on Sales and Units
Method – 3: Filtering DataFrame based on Date value
If we want to filter our data frame based on a certain date value, for example here we are trying to get all the results based on a particular date, in our case the results after the date ’03/10/21′.
#Filter a DataFrame based on specific date
date_filter = df[df['Date'] > '03/10/21']
print(date_filter.head())
Filter on Date
Method – 4: Filtering DataFrame based on Date value with multiple conditions
Here we are getting all the results for our Date operation evaluating multiple dates.
#Filter a DataFrame with multiple conditions our Date value
date_filter2 = df[(df['Date'] >= '3/25/2021') & (df['Date'] <'8/17/2021')]
print(date_filter2.head())
Filter on a date with multiple conditions
Method – 5: Filtering DataFrame based on a specific string
Here we are selecting a column called ‘Region’ and getting all the rows that are from the region ‘East’, thus filtering based on a specific string value.
#Filter a DataFrame to a specific string
east = df[df['Region'] == 'East']
print(east.head())
Filter based on a specific string
Method – 6: Filtering DataFrame based on a specific index value in a string
Here we are selecting a column called ‘Region’ and getting all the rows which has the letter ‘E’ as the first character i.e at index 0 in the specified column results.
#Filter a DataFrame to show rows starting with a specfic letter
starting_with_e = df[df['Region'].str[0]== 'E']
print(starting_with_e.head())
Filter based on a specific letter
Method – 7: Filtering DataFrame based on a list of values
Here we are filtering rows in the column ‘Region’ which contains the values ‘West’ as well as ‘East’ and display the combined result. Two methods can be used to perform this filtering namely using a pipe | operator with the corresponding desired set of values with the below syntax OR we can use the .isin() function to filter for the values in a given column, which in our case is the ‘Region’, and provide the list of the desired set of values inside it as a list.
#Filter a DataFrame rows based on list of values
#Method 1:
east_west = df[(df['Region'] == 'West') | (df['Region'] == 'East')]
print(east_west)
#Method 2:
east_west_1 = df[df['Region'].isin(['West', 'East'])]
print(east_west_1.head())
Output of Method -2
Method – 8: Filtering DataFrame rows based on specific values using RegEx
Here we want all the values in the column ‘Region’, which ends with ‘th’ in their string value and display them. In other words, we want our results to show the values of ‘North‘ and ‘South‘ and ignore ‘East’ and ‘West’. The method .str.contains() with the specified values along with the $ RegEx pattern can be used to get the desired results.
For more information please check the Regex Documentation
#Filtering the DataFrame rows using regular expressions(REGEX)
regex_df = df[df['Region'].str.contains('th$')]
print(regex_df.head())
Filter based on REGEX
Method – 9: Filtering DataFrame to check for null
Here, we’ll check for null and not null values in all the columns with the help of isnull() function.
#Filtering to check for null and not null values in all columns
df_null = df[df.isnull().any(axis=1)]
print(df_null.head())
Filter based on NULL or NOT null values
Method – 10: Filtering DataFrame to check for null values in a specific column.
#Filtering to check for null values if any in the 'Units' column
units_df = df[df['Units'].isnull()]
print(units_df.head())
Finding null values on specific columns
Method – 11: Filtering DataFrame to check for not null values in specific columns
#Filtering to check for not null values in the 'Units' column
df_not_null = df[df['Units'].notnull()]
print(df_not_null.head())
Finding not-null values on specific columns
Method – 12: Filtering DataFrame using query() with a condition
#Using query function in pandas
df_query = df.query('Total_Sales > 300')
print(df_query.head())
Filtering values with Query Function
Method – 13: Filtering DataFrame using query() with multiple conditions
#Using query function with multiple conditions in pandas
df_query_1 = df.query('Total_Sales > 300 and Units <18')
print(df_query_1.head())
Filtering multiple columns with Query Function
Method – 14: Filtering our DataFrame using the loc and iloc functions.
#Creating a sample DataFrame for illustrations
import numpy as np
data = pd.DataFrame({"col1" : np.arange(1, 20 ,2)}, index=[19, 18 ,8, 6, 0, 1, 2, 3, 4, 5])
print(data)
sample_data
Explanation: iloc considers rows based on the position of the given index, so that it takes only integers as values.
For more information please check out Pandas Documentation
#Filter with iloc
data.iloc[0 : 5]
Filter using iloc
Explanation: loc considers rows based on index labels
#Filter with loc
data.loc[0 : 5]
Filter using loc
You might be thinking about why the loc function returns 6 rows instead of 5 rows. This is because loc does not produce output based on index position. It considers labels of index only which can be an alphabet as well and includes both starting and endpoint.
Conclusion
So, these were some of the most common filtering methods used in pandas. There are many other filtering methods that could be used, but these are some of the most common.
Link: https://www.askpython.com/python-modules/pandas/filter-pandas-dataframe
#pandas #python #datafame