Category: Programming

Problem Formulation

Given the following DataFrame df:

import pandas as pd df = pd.DataFrame([{'A':1, 'B':2, 'C':2, 'D':4}, {'A':4, 'B':8, 'C':3, 'D':1}, {'A':2, 'B':7, 'C':1, 'D':2}, {'A':3, 'B':5, 'C':1, 'D':2}]) print(df) ''' A B C D
0 1 2 2 4
1 4 8 3 1
2 2 7 1 2
3 3 5 1 2 '''

Challenge: How to apply a function f to each cell in the DataFrame?

For example, you may want to apply a function that replaces all odd values with the value 'odd'.

import pandas as pd df = pd.DataFrame([{'A':1, 'B':2, 'C':2, 'D':4}, {'A':4, 'B':8, 'C':3, 'D':1}, {'A':2, 'B':7, 'C':1, 'D':2}, {'A':3, 'B':5, 'C':1, 'D':2}]) def f(cell): if cell%2 == 1: return 'odd' return cell # ... <Apply Function f to each cell> ... print(df) ''' A B C D
0 odd 2 2 4
1 4 8 odd odd
2 2 odd odd 2
3 odd odd odd 2 '''

Solution: DataFrame applymap()

The Pandas DataFrame df.applymap() method returns a new DataFrame where the function f is applied to each cell of the original DataFrame df. You can pass any function object as a single argument into the df.applymap() function, either defined as a lambda expression or a normal function.

Example 1: Replace Odd Values in DataFrame

Here’s an example where each cell of the DataFrame is checked against whether it is an odd value. If so, it is replaced with the string 'odd':

def f(cell): if cell%2 == 1: return 'odd' return cell df_new = df.applymap(f) print(df_new) ''' A B C D
0 odd 2 2 4
1 4 8 odd odd
2 2 odd odd 2
3 odd odd odd 2 '''

Example 2: Create Two DataFrames with Even and Odd Values Replaced

A slightly advanced example uses two lambda functions to create two new DataFrames where one has all odd and the other has all even values replaced:

import pandas as pd df = pd.DataFrame([{'A':1, 'B':2, 'C':2, 'D':4}, {'A':4, 'B':8, 'C':3, 'D':1}, {'A':2, 'B':7, 'C':1, 'D':2}, {'A':3, 'B':5, 'C':1, 'D':2}]) df_even = df.applymap(lambda x: 'odd' if x%2 else x)
df_odd = df.applymap(lambda x: x if x%2 else 'even') print(df_even) ''' A B C D
0 odd 2 2 4
1 4 8 odd odd
2 2 odd odd 2
3 odd odd odd 2 ''' print(df_odd) ''' A B C D
0 1 even even even
1 even even 3 1
2 even 7 1 even
3 3 5 1 even '''

We used the concept of a ternary operator to concisely define the replacement function using the keyword lambda to create a function object “on the fly”.

Recommended Tutorial: Understanding the Ternary Operator in Python

This tutorial idea was proposed by Finxter student Kyriakos.

Problem Formulation and Solution Overview

In this article, you’ll learn how to ignore (upper/lower/title) case characters when dealing with Strings in Python.

The main reason for ignoring the case of a String is to perform accurate String comparisons or to return accurate search results.

For example, the following three (3) Strings are not identical. If compared with each other, they would return False.

when life gives you lemons, make lemonade. WHEN LIFE GIVES YOU LEMONS, MAKE LEMONADE. When Life Gives You Lemons, Make Lemonade.

This is because each character in the ASCII Table assigns different numeric values for each key or key combination on the keyboard.

This article outlines various ways to ignore the case of Strings.

---

Question: How would we write code to compare Strings?

We can accomplish this task by one of the following options:

• Method 1: Use a lower() and a lambda
• Method 2: Use upper() and List Comprehension
• Method 3: Use title()
• Method 4: Use casefold()
• Method 5: Use lower() and Dictionary Comprehension
• Bonus: Convert CSV of Full Names to title case

---

Method 1: Use lower() and a lambda

This method uses lower() and a lambda to convert a List of Strings to lower case to search for an Employee.

staff = ['Andy', 'MAci', 'SteVe', 'DarCY', 'Harvey']
staff_lower = list((map(lambda x: x.lower(), staff)))
print(staff_lower)

Above declares a List of Rivers Clothing Employees. If we were searching for an Employee, such as Darcy, using this List, no results would return as Darcy and DarCY are not the same. Let’s fix this issue.

Info: A lambda function is a one-liner that, in this case, converts each List element in staff to lower case. Then, converts to a map() object and back to a List. The output saves to staff_lower and is output to the terminal.

['andy', 'maci', 'steve', 'darcy', 'harvey']

If a comparison was made now, it would find the correct Employee.

if ('Darcy'.lower() == staff_lower[3]): print('Match Found!')

Match Found!

---

Method 2: Use upper() and List Comprehension

This method uses upper() and a list comprehension to convert a List of Strings to upper case and search for an Employee.

brands = ['ChaNnel', 'CLINique', 'DIOR', 'Lancome', 'MurAD']
brands_upper = [x.upper() for x in brands]
print(brands_upper)

Above declares a List of the Top five (5) Beauty Brands in the world. If we were searching for a Company, such as Channel, using this List, no results would return as Channel and ChaNnel are not the same. Let’s fix this issue.

A List Comprehension is called and converts each element in brands to upper case. The results are saved to brands_upper and output to the terminal.

['CHANEL', 'CLINIQUE', 'DIOR', 'LANCOME', 'MURAD']

If a comparison was made now, it would find the correct Brand.

my_fav = 'Murad'
if (my_fav.upper() == brands_upper[4]): print(f'{my_fav} Found!')

Murad Found!

---

Method 3: Use title()

This method uses title() to convert each word in a String to upper case. Great use of this is to convert a Full Name to title case. This ensures both the First, Middle and Last Names are as expected.

full_name = 'jessica a. barker'.title()
print(full_name)

Above accepts a string, appends the title() method to the string, saves it to full_name and outputs to the terminal.

Jessica A. Barker

---

Method 4: Use casefold() and a Dictionary

This method uses casefold() to convert a String to lower case. This method does more than lower(): it uses Unicode Case Folding Rules.

phrase = 'Nichts ist unmöglich für den Unwilligen!'
cfold = phrase.casefold()
print(cfold)

Above creates a quote in German and saves it to phrase. This ensures all characters are correctly converted to lower case, thus making a caseless match.

nichts ist unmöglich für den unwilligen!

What does this say?

---

Method 5: Use lower() and Dictionary Comprehension

This method uses Use lower() to convert Dictionary values into lower case using Dictionary Comprehension.

employees = {'Sandy': 'Coder', 'Kevin': 'Network Specialist', 'Amy': 'Designer'}
result = {k: v.lower() for k, v in employees.items()}
print(result)

Above creates a Dictionary containing Rivers Employees Names and associated Job Title. This saves to employees.

Next, Dictionary Comprehension is used to loop through the values in the key:value Dictionary pairs and convert the values to lower case. The output saves to result and is output to the terminal.

{'Sandy': 'coder', 'Kevin': 'network specialist', 'Amy': 'designer'}

---

Bonus: CSV Column to title() case

In this Bonus section, a CSV file you are working with contains a column of Full Names. You want to ensure all data entered in this column is in title() case. This can be accomplished by running the following code.

Contents of finxter_users.csv file

FID	Full_Name
30022145	sally jenkins
30022192	joyce hamilton
30022331	allan thompson
30022345	harry stiller
30022157	ben hawkins

import pandas as pd users = pd.read_csv('finxter_users.csv')
users['Full_Name'] = users['Full_Name'].str.title()
print(users)

Above imports the Pandas library. For installation instructions, click here.

Next, the finxter_users.csv file (contents shown above) is read and saved to the DataFrame users.

The highlighted line directly accesses all values in the users['Full_Name'] column and converts each entry to title() case. This saves back to users['Full_Name'] and is output to the terminal.

	FID	Full_Name
0	30022145	Sally Jenkins
1	30022192	Joyce Hamilton
2	30022331	Allan Thompson
3	30022345	Harry Stiller
4	30022157	Ben Hawkins

Note: Don’t forget to save the DataFrame to keep the changes.

---

Summary

These six (6) methods of ignoring cases in Strings should give you enough information to select the best one for your coding requirements.

Programmer Humor

Question: Why do programmers always mix up Halloween and Christmas?
Answer: Because Oct 31 equals Dec 25.

(If you didn’t get this, read our articles on the oct() and int() Python built-in functions!)

What is KML?

Definition: The Keyhole Markup Language (KML) is a file format for displaying geographic data in Google Earth or other so-called “Earth Browsers”. Similarly to XML, KML uses a tag-based structure with nested elements and attributes.

How to Convert KML to CSV in Python?

You can convert a .kml to a .csv file in Python by using the BeautifulSoup and the csv libraries. You use the former to read the XML-structured KML file and the latter to write the CSV file row by row.

Here’s the code example inspired but modified from this GitHub repository. You can copy&paste it in the directory where your KML file resides and change the input and output filenames at the beginning to convert your own KML to a CSV in Python:

from bs4 import BeautifulSoup
import csv infile = 'my_file.kml'
outfile = 'my_file.csv' with open(infile, 'r') as f: s = BeautifulSoup(f, 'xml') with open(outfile, 'wb') as csvfile: writer = csv.writer(csvfile) for coords in s.find_all('coordinates'): # Take coordinate string from KML and break it up into [Lat,Lon,Lat,Lon...] to get CSV row space_splits = coords.string.split(" ") row = [] for split in space_splits[1:]: # Note: because of the space between <coordinates>" "-80.123, we slice [1:] comma_split = split.split(',') # lattitude row.append(comma_split[1]) # longitude row.append(comma_split[0]) writer.writerow(row)

Example Conversion

We use the following sample KML file as 'my_file.kml':

<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2"> <Document> <name>KML Samples</name> <open>1</open> <description>Unleash your creativity with the help of these examples!</description> <Style id="downArrowIcon"> <IconStyle> <Icon> <href>http://maps.google.com/mapfiles/kml/pal4/icon28.png</href> </Icon> </IconStyle> </Style> <Style id="globeIcon"> <IconStyle> <Icon> <href>http://maps.google.com/mapfiles/kml/pal3/icon19.png</href> </Icon> </IconStyle> <LineStyle> <width>2</width> </LineStyle> </Style> <Style id="transPurpleLineGreenPoly"> <LineStyle> <color>7fff00ff</color> <width>4</width> </LineStyle> <PolyStyle> <color>7f00ff00</color> </PolyStyle> </Style> <Style id="yellowLineGreenPoly"> <LineStyle> <color>7f00ffff</color> <width>4</width> </LineStyle> <PolyStyle> <color>7f00ff00</color> </PolyStyle> </Style> <Style id="thickBlackLine"> <LineStyle> <color>87000000</color> <width>10</width> </LineStyle> </Style> <Style id="redLineBluePoly"> <LineStyle> <color>ff0000ff</color> </LineStyle> <PolyStyle> <color>ffff0000</color> </PolyStyle> </Style> <Style id="blueLineRedPoly"> <LineStyle> <color>ffff0000</color> </LineStyle> <PolyStyle> <color>ff0000ff</color> </PolyStyle> </Style> <Style id="transRedPoly"> <LineStyle> <width>1.5</width> </LineStyle> <PolyStyle> <color>7d0000ff</color> </PolyStyle> </Style> <Style id="transBluePoly"> <LineStyle> <width>1.5</width> </LineStyle> <PolyStyle> <color>7dff0000</color> </PolyStyle> </Style> <Style id="transGreenPoly"> <LineStyle> <width>1.5</width> </LineStyle> <PolyStyle> <color>7d00ff00</color> </PolyStyle> </Style> <Style id="transYellowPoly"> <LineStyle> <width>1.5</width> </LineStyle> <PolyStyle> <color>7d00ffff</color> </PolyStyle> </Style> <Style id="noDrivingDirections"> <BalloonStyle> <text><![CDATA[ <b>$[name]</b> <br /><br /> $[description] ]]></text> </BalloonStyle> </Style> <Folder> <name>Placemarks</name> <description>These are just some of the different kinds of placemarks with which you can mark your favorite places</description> <LookAt> <longitude>-122.0839597145766</longitude> <latitude>37.42222904525232</latitude> <altitude>0</altitude> <heading>-148.4122922628044</heading> <tilt>40.5575073395506</tilt> <range>500.6566641072245</range> </LookAt> <Placemark> <name>Simple placemark</name> <description>Attached to the ground. Intelligently places itself at the height of the underlying terrain.</description> <Point> <coordinates>-122.0822035425683,37.42228990140251,0</coordinates> </Point> </Placemark> <Placemark> <name>Floating placemark</name> <visibility>0</visibility> <description>Floats a defined distance above the ground.</description> <LookAt> <longitude>-122.0839597145766</longitude> <latitude>37.42222904525232</latitude> <altitude>0</altitude> <heading>-148.4122922628044</heading> <tilt>40.5575073395506</tilt> <range>500.6566641072245</range> </LookAt> <styleUrl>#downArrowIcon</styleUrl> <Point> <altitudeMode>relativeToGround</altitudeMode> <coordinates>-122.084075,37.4220033612141,50</coordinates> </Point> </Placemark> <Placemark> <name>Extruded placemark</name> <visibility>0</visibility> <description>Tethered to the ground by a customizable &quot;tail&quot;</description> <LookAt> <longitude>-122.0845787421525</longitude> <latitude>37.42215078737763</latitude> <altitude>0</altitude> <heading>-148.4126684946234</heading> <tilt>40.55750733918048</tilt> <range>365.2646606980322</range> </LookAt> <styleUrl>#globeIcon</styleUrl> <Point> <extrude>1</extrude> <altitudeMode>relativeToGround</altitudeMode> <coordinates>-122.0857667006183,37.42156927867553,50</coordinates> </Point> </Placemark> </Folder> <Folder> <name>Styles and Markup</name> <visibility>0</visibility> <description>With KML it is easy to create rich, descriptive markup to annotate and enrich your placemarks</description> <LookAt> <longitude>-122.0845787422371</longitude> <latitude>37.42215078726837</latitude> <altitude>0</altitude> <heading>-148.4126777488172</heading> <tilt>40.55750733930874</tilt> <range>365.2646826292919</range> </LookAt> <styleUrl>#noDrivingDirections</styleUrl> <Document> <name>Highlighted Icon</name> <visibility>0</visibility> <description>Place your mouse over the icon to see it display the new icon</description> <LookAt> <longitude>-122.0856552124024</longitude> <latitude>37.4224281311035</latitude> <altitude>0</altitude> <heading>0</heading> <tilt>0</tilt> <range>265.8520424250024</range> </LookAt> <Style id="highlightPlacemark"> <IconStyle> <Icon> <href>http://maps.google.com/mapfiles/kml/paddle/red-stars.png</href> </Icon> </IconStyle> </Style> <Style id="normalPlacemark"> <IconStyle> <Icon> <href>http://maps.google.com/mapfiles/kml/paddle/wht-blank.png</href> </Icon> </IconStyle> </Style> <StyleMap id="exampleStyleMap"> <Pair> <key>normal</key> <styleUrl>#normalPlacemark</styleUrl> </Pair> <Pair> <key>highlight</key> <styleUrl>#highlightPlacemark</styleUrl> </Pair> </StyleMap> <Placemark> <name>Roll over this icon</name> <visibility>0</visibility> <styleUrl>#exampleStyleMap</styleUrl> <Point> <coordinates>-122.0856545755255,37.42243077405461,0</coordinates> </Point> </Placemark> </Document> <Placemark> <name>Descriptive HTML</name> <visibility>0</visibility> <description><![CDATA[Click on the blue link!<br><br>
Placemark descriptions can be enriched by using many standard HTML tags.<br>
For example:
<hr>
Styles:<br>
<i>Italics</i>, <b>Bold</b>, <u>Underlined</u>, <s>Strike Out</s>, subscript<sub>subscript</sub>, superscript<sup>superscript</sup>, <big>Big</big>, <small>Small</small>, <tt>Typewriter</tt>, <em>Emphasized</em>, <strong>Strong</strong>, <code>Code</code>
<hr>
Fonts:<br> <font color="red">red by name</font>, <font color="#408010">leaf green by hexadecimal RGB</font>
<br>
<font size=1>size 1</font>, <font size=2>size 2</font>, <font size=3>size 3</font>, <font size=4>size 4</font>, <font size=5>size 5</font>, <font size=6>size 6</font>, <font size=7>size 7</font>
<br>
<font face=times>Times</font>, <font face=verdana>Verdana</font>, <font face=arial>Arial</font><br>
<hr>
Links: <br>
<a href="http://earth.google.com/">Google Earth!</a>
<br> or: Check out our website at www.google.com
<hr>
Alignment:<br>
<p align=left>left</p>
<p align=center>center</p>
<p align=right>right</p>
<hr>
Ordered Lists:<br>
<ol><li>First</li><li>Second</li><li>Third</li></ol>
<ol type="a"><li>First</li><li>Second</li><li>Third</li></ol>
<ol type="A"><li>First</li><li>Second</li><li>Third</li></ol>
<hr>
Unordered Lists:<br>
<ul><li>A</li><li>B</li><li>C</li></ul>
<ul type="circle"><li>A</li><li>B</li><li>C</li></ul>
<ul type="square"><li>A</li><li>B</li><li>C</li></ul>
<hr>
Definitions:<br>
<dl>
<dt>Google:</dt><dd>The best thing since sliced bread</dd>
</dl>
<hr>
Centered:<br><center>
Time present and time past<br>
Are both perhaps present in time future,<br>
And time future contained in time past.<br>
If all time is eternally present<br>
All time is unredeemable.<br>
</center>
<hr>
Block Quote:
<br>
<blockquote>
We shall not cease from exploration<br>
And the end of all our exploring<br>
Will be to arrive where we started<br>
And know the place for the first time.<br>
<i> – T.S. Eliot</i>
</blockquote>
<br>
<hr>
Headings:<br>
<h1>Header 1</h1>
<h2>Header 2</h2>
<h3>Header 3</h3>
<h3>Header 4</h4>
<h3>Header 5</h5>
<hr>
Images:<br>
<i>Remote image</i><br>
<img src="//developers.google.com/kml/documentation/images/googleSample.png"><br>
<i>Scaled image</i><br>
<img src="//developers.google.com/kml/documentation/images/googleSample.png" width=100><br>
<hr>
Simple Tables:<br>
<table border="1" padding="1">
<tr><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td></tr>
<tr><td>a</td><td>b</td><td>c</td><td>d</td><td>e</td></tr>
</table>
<br>
[Did you notice that double-clicking on the placemark doesn't cause the viewer to take you anywhere? This is because it is possible to directly author a "placeless placemark". If you look at the code for this example, you will see that it has neither a point coordinate nor a LookAt element.]]]></description> </Placemark> </Folder> <Folder> <name>Ground Overlays</name> <visibility>0</visibility> <description>Examples of ground overlays</description> <GroundOverlay> <name>Large-scale overlay on terrain</name> <visibility>0</visibility> <description>Overlay shows Mount Etna erupting on July 13th, 2001.</description> <LookAt> <longitude>15.02468937557116</longitude> <latitude>37.67395167941667</latitude> <altitude>0</altitude> <heading>-16.5581842842829</heading> <tilt>58.31228652890705</tilt> <range>30350.36838438907</range> </LookAt> <Icon> <href>http://developers.google.com/kml/documentation/images/etna.jpg</href> </Icon> <LatLonBox> <north>37.91904192681665</north> <south>37.46543388598137</south> <east>15.35832653742206</east> <west>14.60128369746704</west> <rotation>-0.1556640799496235</rotation> </LatLonBox> </GroundOverlay> </Folder> <Folder> <name>Screen Overlays</name> <visibility>0</visibility> <description>Screen overlays have to be authored directly in KML. These examples illustrate absolute and dynamic positioning in screen space.</description> <ScreenOverlay> <name>Simple crosshairs</name> <visibility>0</visibility> <description>This screen overlay uses fractional positioning to put the image in the exact center of the screen</description> <Icon> <href>http://developers.google.com/kml/documentation/images/crosshairs.png</href> </Icon> <overlayXY x="0.5" y="0.5" xunits="fraction" yunits="fraction"/> <screenXY x="0.5" y="0.5" xunits="fraction" yunits="fraction"/> <rotationXY x="0.5" y="0.5" xunits="fraction" yunits="fraction"/> <size x="0" y="0" xunits="pixels" yunits="pixels"/> </ScreenOverlay> <ScreenOverlay> <name>Absolute Positioning: Top left</name> <visibility>0</visibility> <Icon> <href>http://developers.google.com/kml/documentation/images/top_left.jpg</href> </Icon> <overlayXY x="0" y="1" xunits="fraction" yunits="fraction"/> <screenXY x="0" y="1" xunits="fraction" yunits="fraction"/> <rotationXY x="0" y="0" xunits="fraction" yunits="fraction"/> <size x="0" y="0" xunits="fraction" yunits="fraction"/> </ScreenOverlay> <ScreenOverlay> <name>Absolute Positioning: Top right</name> <visibility>0</visibility> <Icon> <href>http://developers.google.com/kml/documentation/images/top_right.jpg</href> </Icon> <overlayXY x="1" y="1" xunits="fraction" yunits="fraction"/> <screenXY x="1" y="1" xunits="fraction" yunits="fraction"/> <rotationXY x="0" y="0" xunits="fraction" yunits="fraction"/> <size x="0" y="0" xunits="fraction" yunits="fraction"/> </ScreenOverlay> <ScreenOverlay> <name>Absolute Positioning: Bottom left</name> <visibility>0</visibility> <Icon> <href>http://developers.google.com/kml/documentation/images/bottom_left.jpg</href> </Icon> <overlayXY x="0" y="-1" xunits="fraction" yunits="fraction"/> <screenXY x="0" y="0" xunits="fraction" yunits="fraction"/> <rotationXY x="0" y="0" xunits="fraction" yunits="fraction"/> <size x="0" y="0" xunits="fraction" yunits="fraction"/> </ScreenOverlay> <ScreenOverlay> <name>Absolute Positioning: Bottom right</name> <visibility>0</visibility> <Icon> <href>http://developers.google.com/kml/documentation/images/bottom_right.jpg</href> </Icon> <overlayXY x="1" y="-1" xunits="fraction" yunits="fraction"/> <screenXY x="1" y="0" xunits="fraction" yunits="fraction"/> <rotationXY x="0" y="0" xunits="fraction" yunits="fraction"/> <size x="0" y="0" xunits="fraction" yunits="fraction"/> </ScreenOverlay> <ScreenOverlay> <name>Dynamic Positioning: Top of screen</name> <visibility>0</visibility> <Icon> <href>http://developers.google.com/kml/documentation/images/dynamic_screenoverlay.jpg</href> </Icon> <overlayXY x="0" y="1" xunits="fraction" yunits="fraction"/> <screenXY x="0" y="1" xunits="fraction" yunits="fraction"/> <rotationXY x="0" y="0" xunits="fraction" yunits="fraction"/> <size x="1" y="0.2" xunits="fraction" yunits="fraction"/> </ScreenOverlay> <ScreenOverlay> <name>Dynamic Positioning: Right of screen</name> <visibility>0</visibility> <Icon> <href>http://developers.google.com/kml/documentation/images/dynamic_right.jpg</href> </Icon> <overlayXY x="1" y="1" xunits="fraction" yunits="fraction"/> <screenXY x="1" y="1" xunits="fraction" yunits="fraction"/> <rotationXY x="0" y="0" xunits="fraction" yunits="fraction"/> <size x="0" y="1" xunits="fraction" yunits="fraction"/> </ScreenOverlay> </Folder> <Folder> <name>Paths</name> <visibility>0</visibility> <description>Examples of paths. Note that the tessellate tag is by default set to 0. If you want to create tessellated lines, they must be authored (or edited) directly in KML.</description> <Placemark> <name>Tessellated</name> <visibility>0</visibility> <description><![CDATA[If the <tessellate> tag has a value of 1, the line will contour to the underlying terrain]]></description> <LookAt> <longitude>-112.0822680013139</longitude> <latitude>36.09825589333556</latitude> <altitude>0</altitude> <heading>103.8120432044965</heading> <tilt>62.04855796276328</tilt> <range>2889.145007690472</range> </LookAt> <LineString> <tessellate>1</tessellate> <coordinates> -112.0814237830345,36.10677870477137,0 -112.0870267752693,36.0905099328766,0 </coordinates> </LineString> </Placemark> <Placemark> <name>Untessellated</name> <visibility>0</visibility> <description><![CDATA[If the <tessellate> tag has a value of 0, the line follow a simple straight-line path from point to point]]></description> <LookAt> <longitude>-112.0822680013139</longitude> <latitude>36.09825589333556</latitude> <altitude>0</altitude> <heading>103.8120432044965</heading> <tilt>62.04855796276328</tilt> <range>2889.145007690472</range> </LookAt> <LineString> <tessellate>0</tessellate> <coordinates> -112.080622229595,36.10673460007995,0 -112.085242575315,36.09049598612422,0 </coordinates> </LineString> </Placemark> <Placemark> <name>Absolute</name> <visibility>0</visibility> <description>Transparent purple line</description> <LookAt> <longitude>-112.2719329043177</longitude> <latitude>36.08890633450894</latitude> <altitude>0</altitude> <heading>-106.8161545998597</heading> <tilt>44.60763714063257</tilt> <range>2569.386744398339</range> </LookAt> <styleUrl>#transPurpleLineGreenPoly</styleUrl> <LineString> <tessellate>1</tessellate> <altitudeMode>absolute</altitudeMode> <coordinates> -112.265654928602,36.09447672602546,2357 -112.2660384528238,36.09342608838671,2357 -112.2668139013453,36.09251058776881,2357 -112.2677826834445,36.09189827357996,2357 -112.2688557510952,36.0913137941187,2357 -112.2694810717219,36.0903677207521,2357 -112.2695268555611,36.08932171487285,2357 -112.2690144567276,36.08850916060472,2357 -112.2681528815339,36.08753813597956,2357 -112.2670588176031,36.08682685262568,2357 -112.2657374587321,36.08646312301303,2357 </coordinates> </LineString> </Placemark> <Placemark> <name>Absolute Extruded</name> <visibility>0</visibility> <description>Transparent green wall with yellow outlines</description> <LookAt> <longitude>-112.2643334742529</longitude> <latitude>36.08563154742419</latitude> <altitude>0</altitude> <heading>-125.7518698668815</heading> <tilt>44.61038665812578</tilt> <range>4451.842204068102</range> </LookAt> <styleUrl>#yellowLineGreenPoly</styleUrl> <LineString> <extrude>1</extrude> <tessellate>1</tessellate> <altitudeMode>absolute</altitudeMode> <coordinates> -112.2550785337791,36.07954952145647,2357 -112.2549277039738,36.08117083492122,2357 -112.2552505069063,36.08260761307279,2357 -112.2564540158376,36.08395660588506,2357 -112.2580238976449,36.08511401044813,2357 -112.2595218489022,36.08584355239394,2357 -112.2608216347552,36.08612634548589,2357 -112.262073428656,36.08626019085147,2357 -112.2633204928495,36.08621519860091,2357 -112.2644963846444,36.08627897945274,2357 -112.2656969554589,36.08649599090644,2357 </coordinates> </LineString> </Placemark> <Placemark> <name>Relative</name> <visibility>0</visibility> <description>Black line (10 pixels wide), height tracks terrain</description> <LookAt> <longitude>-112.2580438551384</longitude> <latitude>36.1072674824385</latitude> <altitude>0</altitude> <heading>4.947421249553717</heading> <tilt>44.61324882043339</tilt> <range>2927.61105910266</range> </LookAt> <styleUrl>#thickBlackLine</styleUrl> <LineString> <tessellate>1</tessellate> <altitudeMode>relativeToGround</altitudeMode> <coordinates> -112.2532845153347,36.09886943729116,645 -112.2540466121145,36.09919570465255,645 -112.254734666947,36.09984998366178,645 -112.255493345654,36.10051310621746,645 -112.2563157098468,36.10108441943419,645 -112.2568033076439,36.10159722088088,645 -112.257494011321,36.10204323542867,645 -112.2584106072308,36.10229131995655,645 -112.2596588987972,36.10240001286358,645 -112.2610581199487,36.10213176873407,645 -112.2626285262793,36.10157011437219,645 </coordinates> </LineString> </Placemark> <Placemark> <name>Relative Extruded</name> <visibility>0</visibility> <description>Opaque blue walls with red outline, height tracks terrain</description> <LookAt> <longitude>-112.2683594333433</longitude> <latitude>36.09884362144909</latitude> <altitude>0</altitude> <heading>-72.24271551768405</heading> <tilt>44.60855445139561</tilt> <range>2184.193522571467</range> </LookAt> <styleUrl>#redLineBluePoly</styleUrl> <LineString> <extrude>1</extrude> <tessellate>1</tessellate> <altitudeMode>relativeToGround</altitudeMode> <coordinates> -112.2656634181359,36.09445214722695,630 -112.2652238941097,36.09520916122063,630 -112.2645079986395,36.09580763864907,630 -112.2638827428817,36.09628572284063,630 -112.2635746835406,36.09679275951239,630 -112.2635711822407,36.09740038871899,630 -112.2640296531825,36.09804913435539,630 -112.264327720538,36.09880337400301,630 -112.2642436562271,36.09963644790288,630 -112.2639148687042,36.10055381117246,630 -112.2626894973474,36.10149062823369,630 </coordinates> </LineString> </Placemark> </Folder> <Folder> <name>Polygons</name> <visibility>0</visibility> <description>Examples of polygon shapes</description> <Folder> <name>Google Campus</name> <visibility>0</visibility> <description>A collection showing how easy it is to create 3-dimensional buildings</description> <LookAt> <longitude>-122.084120030116</longitude> <latitude>37.42174011925477</latitude> <altitude>0</altitude> <heading>-34.82469740081282</heading> <tilt>53.454348562403</tilt> <range>276.7870053764046</range> </LookAt> <Placemark> <name>Building 40</name> <visibility>0</visibility> <styleUrl>#transRedPoly</styleUrl> <Polygon> <extrude>1</extrude> <altitudeMode>relativeToGround</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -122.0848938459612,37.42257124044786,17 -122.0849580979198,37.42211922626856,17 -122.0847469573047,37.42207183952619,17 -122.0845725380962,37.42209006729676,17 -122.0845954886723,37.42215932700895,17 -122.0838521118269,37.42227278564371,17 -122.083792243335,37.42203539112084,17 -122.0835076656616,37.42209006957106,17 -122.0834709464152,37.42200987395161,17 -122.0831221085748,37.4221046494946,17 -122.0829247374572,37.42226503990386,17 -122.0829339169385,37.42231242843094,17 -122.0833837359737,37.42225046087618,17 -122.0833607854248,37.42234159228745,17 -122.0834204551642,37.42237075460644,17 -122.083659133885,37.42251292011001,17 -122.0839758438952,37.42265873093781,17 -122.0842374743331,37.42265143972521,17 -122.0845036949503,37.4226514386435,17 -122.0848020460801,37.42261133916315,17 -122.0847882750515,37.42256395055121,17 -122.0848938459612,37.42257124044786,17 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> <Placemark> <name>Building 41</name> <visibility>0</visibility> <styleUrl>#transBluePoly</styleUrl> <Polygon> <extrude>1</extrude> <altitudeMode>relativeToGround</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -122.0857412771483,37.42227033155257,17 -122.0858169768481,37.42231408832346,17 -122.085852582875,37.42230337469744,17 -122.0858799945639,37.42225686138789,17 -122.0858860101409,37.4222311076138,17 -122.0858069157288,37.42220250173855,17 -122.0858379542653,37.42214027058678,17 -122.0856732640519,37.42208690214408,17 -122.0856022926407,37.42214885429042,17 -122.0855902778436,37.422128290487,17 -122.0855841672237,37.42208171967246,17 -122.0854852065741,37.42210455874995,17 -122.0855067264352,37.42214267949824,17 -122.0854430712915,37.42212783846172,17 -122.0850990714904,37.42251282407603,17 -122.0856769818632,37.42281815323651,17 -122.0860162273783,37.42244918858722,17 -122.0857260327004,37.42229239604253,17 -122.0857412771483,37.42227033155257,17 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> <Placemark> <name>Building 42</name> <visibility>0</visibility> <styleUrl>#transGreenPoly</styleUrl> <Polygon> <extrude>1</extrude> <altitudeMode>relativeToGround</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -122.0857862287242,37.42136208886969,25 -122.0857312990603,37.42136935989481,25 -122.0857312992918,37.42140934910903,25 -122.0856077073679,37.42138390166565,25 -122.0855802426516,37.42137299550869,25 -122.0852186221971,37.42137299504316,25 -122.0852277765639,37.42161656508265,25 -122.0852598189347,37.42160565894403,25 -122.0852598185499,37.42168200156,25 -122.0852369311478,37.42170017860346,25 -122.0852643957828,37.42176197982575,25 -122.0853239032746,37.42176198013907,25 -122.0853559454324,37.421852864452,25 -122.0854108752463,37.42188921823734,25 -122.0854795379357,37.42189285337048,25 -122.0855436229819,37.42188921797546,25 -122.0856260178042,37.42186013499926,25 -122.085937287963,37.42186013453605,25 -122.0859428718666,37.42160898590042,25 -122.0859655469861,37.42157992759144,25 -122.0858640462341,37.42147115002957,25 -122.0858548911215,37.42140571326184,25 -122.0858091162768,37.4214057134039,25 -122.0857862287242,37.42136208886969,25 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> <Placemark> <name>Building 43</name> <visibility>0</visibility> <styleUrl>#transYellowPoly</styleUrl> <Polygon> <extrude>1</extrude> <altitudeMode>relativeToGround</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -122.0844371128284,37.42177253003091,19 -122.0845118855746,37.42191111542896,19 -122.0850470999805,37.42178755121535,19 -122.0850719913391,37.42143663023161,19 -122.084916406232,37.42137237822116,19 -122.0842193868167,37.42137237801626,19 -122.08421938659,37.42147617161496,19 -122.0838086419991,37.4214613409357,19 -122.0837899728564,37.42131306410796,19 -122.0832796534698,37.42129328840593,19 -122.0832609819207,37.42139213944298,19 -122.0829373621737,37.42137236399876,19 -122.0829062425667,37.42151569778871,19 -122.0828502269665,37.42176282576465,19 -122.0829435788635,37.42176776969635,19 -122.083217411188,37.42179248552686,19 -122.0835970430103,37.4217480074456,19 -122.0839455556771,37.42169364237603,19 -122.0840077894637,37.42176283815853,19 -122.084113587521,37.42174801104392,19 -122.0840762473784,37.42171341292375,19 -122.0841447047739,37.42167881534569,19 -122.084144704223,37.42181720660197,19 -122.0842503333074,37.4218170700446,19 -122.0844371128284,37.42177253003091,19 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> </Folder> <Folder> <name>Extruded Polygon</name> <description>A simple way to model a building</description> <Placemark> <name>The Pentagon</name> <LookAt> <longitude>-77.05580139178142</longitude> <latitude>38.870832443487</latitude> <heading>59.88865561738225</heading> <tilt>48.09646074797388</tilt> <range>742.0552506670548</range> </LookAt> <Polygon> <extrude>1</extrude> <altitudeMode>relativeToGround</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -77.05788457660967,38.87253259892824,100 -77.05465973756702,38.87291016281703,100 -77.05315536854791,38.87053267794386,100 -77.05552622493516,38.868757801256,100 -77.05844056290393,38.86996206506943,100 -77.05788457660967,38.87253259892824,100 </coordinates> </LinearRing> </outerBoundaryIs> <innerBoundaryIs> <LinearRing> <coordinates> -77.05668055019126,38.87154239798456,100 -77.05542625960818,38.87167890344077,100 -77.05485125901024,38.87076535397792,100 -77.05577677433152,38.87008686581446,100 -77.05691162017543,38.87054446963351,100 -77.05668055019126,38.87154239798456,100 </coordinates> </LinearRing> </innerBoundaryIs> </Polygon> </Placemark> </Folder> <Folder> <name>Absolute and Relative</name> <visibility>0</visibility> <description>Four structures whose roofs meet exactly. Turn on/off terrain to see the difference between relative and absolute positioning.</description> <LookAt> <longitude>-112.3348969157552</longitude> <latitude>36.14845533214919</latitude> <altitude>0</altitude> <heading>-86.91235037566909</heading> <tilt>49.30695423894192</tilt> <range>990.6761201087104</range> </LookAt> <Placemark> <name>Absolute</name> <visibility>0</visibility> <styleUrl>#transBluePoly</styleUrl> <Polygon> <tessellate>1</tessellate> <altitudeMode>absolute</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -112.3372510731295,36.14888505105317,1784 -112.3356128688403,36.14781540589019,1784 -112.3368169371048,36.14658677734382,1784 -112.3384408457543,36.14762778914076,1784 -112.3372510731295,36.14888505105317,1784 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> <Placemark> <name>Absolute Extruded</name> <visibility>0</visibility> <styleUrl>#transRedPoly</styleUrl> <Polygon> <extrude>1</extrude> <tessellate>1</tessellate> <altitudeMode>absolute</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -112.3396586818843,36.14637618647505,1784 -112.3380597654315,36.14531751871353,1784 -112.3368254237788,36.14659596244607,1784 -112.3384555043203,36.14762621763982,1784 -112.3396586818843,36.14637618647505,1784 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> <Placemark> <name>Relative</name> <visibility>0</visibility> <LookAt> <longitude>-112.3350152490417</longitude> <latitude>36.14943123077423</latitude> <altitude>0</altitude> <heading>-118.9214100848499</heading> <tilt>37.92486261093203</tilt> <range>345.5169113679813</range> </LookAt> <styleUrl>#transGreenPoly</styleUrl> <Polygon> <tessellate>1</tessellate> <altitudeMode>relativeToGround</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -112.3349463145932,36.14988705767721,100 -112.3354019540677,36.14941108398372,100 -112.3344428289146,36.14878490381308,100 -112.3331289492913,36.14780840132443,100 -112.3317019516947,36.14680755678357,100 -112.331131440106,36.1474173426228,100 -112.332616324338,36.14845453364654,100 -112.3339876620524,36.14926570522069,100 -112.3349463145932,36.14988705767721,100 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> <Placemark> <name>Relative Extruded</name> <visibility>0</visibility> <LookAt> <longitude>-112.3351587892382</longitude> <latitude>36.14979247129029</latitude> <altitude>0</altitude> <heading>-55.42811560891606</heading> <tilt>56.10280503739589</tilt> <range>401.0997279712519</range> </LookAt> <styleUrl>#transYellowPoly</styleUrl> <Polygon> <extrude>1</extrude> <tessellate>1</tessellate> <altitudeMode>relativeToGround</altitudeMode> <outerBoundaryIs> <LinearRing> <coordinates> -112.3348783983763,36.1514008468736,100 -112.3372535345629,36.14888517553886,100 -112.3356068927954,36.14781612679284,100 -112.3350034807972,36.14846469024177,100 -112.3358353861232,36.1489624162954,100 -112.3345888301373,36.15026229372507,100 -112.3337937856278,36.14978096026463,100 -112.3331798208424,36.1504472788618,100 -112.3348783983763,36.1514008468736,100 </coordinates> </LinearRing> </outerBoundaryIs> </Polygon> </Placemark> </Folder> </Folder> </Document>
</kml>

The following is the resulting CSV after running the above code snippet (new CSV file: 'my_file.csv'):

36.10677870477137,-112.0814237830345,36.0905099328766,-112.0870267752693
36.10673460007995,-112.080622229595,36.09049598612422,-112.085242575315
36.09447672602546,-112.265654928602,36.09342608838671,-112.2660384528238,36.09251058776881,-112.2668139013453,36.09189827357996,-112.2677826834445,36.0913137941187,-112.2688557510952,36.0903677207521,-112.2694810717219,36.08932171487285,-112.2695268555611,36.08850916060472,-112.2690144567276,36.08753813597956,-112.2681528815339,36.08682685262568,-112.2670588176031,36.08646312301303,-112.2657374587321
36.07954952145647,-112.2550785337791,36.08117083492122,-112.2549277039738,36.08260761307279,-112.2552505069063,36.08395660588506,-112.2564540158376,36.08511401044813,-112.2580238976449,36.08584355239394,-112.2595218489022,36.08612634548589,-112.2608216347552,36.08626019085147,-112.262073428656,36.08621519860091,-112.2633204928495,36.08627897945274,-112.2644963846444,36.08649599090644,-112.2656969554589
36.09886943729116,-112.2532845153347,36.09919570465255,-112.2540466121145,36.09984998366178,-112.254734666947,36.10051310621746,-112.255493345654,36.10108441943419,-112.2563157098468,36.10159722088088,-112.2568033076439,36.10204323542867,-112.257494011321,36.10229131995655,-112.2584106072308,36.10240001286358,-112.2596588987972,36.10213176873407,-112.2610581199487,36.10157011437219,-112.2626285262793
36.09445214722695,-112.2656634181359,36.09520916122063,-112.2652238941097,36.09580763864907,-112.2645079986395,36.09628572284063,-112.2638827428817,36.09679275951239,-112.2635746835406,36.09740038871899,-112.2635711822407,36.09804913435539,-112.2640296531825,36.09880337400301,-112.264327720538,36.09963644790288,-112.2642436562271,36.10055381117246,-112.2639148687042,36.10149062823369,-112.2626894973474
37.42257124044786,-122.0848938459612,37.42211922626856,-122.0849580979198,37.42207183952619,-122.0847469573047,37.42209006729676,-122.0845725380962,37.42215932700895,-122.0845954886723,37.42227278564371,-122.0838521118269,37.42203539112084,-122.083792243335,37.42209006957106,-122.0835076656616,37.42200987395161,-122.0834709464152,37.4221046494946,-122.0831221085748,37.42226503990386,-122.0829247374572,37.42231242843094,-122.0829339169385,37.42225046087618,-122.0833837359737,37.42234159228745,-122.0833607854248,37.42237075460644,-122.0834204551642,37.42251292011001,-122.083659133885,37.42265873093781,-122.0839758438952,37.42265143972521,-122.0842374743331,37.4226514386435,-122.0845036949503,37.42261133916315,-122.0848020460801,37.42256395055121,-122.0847882750515,37.42257124044786,-122.0848938459612
37.42227033155257,-122.0857412771483,37.42231408832346,-122.0858169768481,37.42230337469744,-122.085852582875,37.42225686138789,-122.0858799945639,37.4222311076138,-122.0858860101409,37.42220250173855,-122.0858069157288,37.42214027058678,-122.0858379542653,37.42208690214408,-122.0856732640519,37.42214885429042,-122.0856022926407,37.422128290487,-122.0855902778436,37.42208171967246,-122.0855841672237,37.42210455874995,-122.0854852065741,37.42214267949824,-122.0855067264352,37.42212783846172,-122.0854430712915,37.42251282407603,-122.0850990714904,37.42281815323651,-122.0856769818632,37.42244918858722,-122.0860162273783,37.42229239604253,-122.0857260327004,37.42227033155257,-122.0857412771483
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What is GeoJSON?

GeoJSON is an RFC standardized data format to encode geographic data structures such as Point, LineString, Polygon, MultiPoint, MultiLineString, and MultiPolygon. GeoJSON is based on the JavaScript Object Notation (JSON).

Example GeoJSON to CSV

Say, you have the following GeoJSON snippet:

{ "type": "FeatureCollection", "features": [ { "type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}, "properties": { "name": "Location A", "category": "Store" } }, { "type": "Feature", "geometry": {"type": "Point", "coordinates": [-80.24, 40.12]}, "properties": { "name": "Location B", "category": "House" } }, { "type": "Feature", "geometry": {"type": "Point", "coordinates": [ -77.2, 41.427]}, "properties": { "name": "Location C", "category": "Office" } } ] }

You want to convert it to the following CSV format:

latitude,longitude,altitude,geometry,name,category
39.984,-75.343,,Point,Location A,Store
40.12,-80.24,,Point,Location B,House
41.427,-77.2,,Point,Location C,Office

Python GeoJSON to CSV Conversion

The Python code to convert a GeoJSON to a CSV in Python uses a combination of the json and csv packages.

import json
import csv geo_filename = 'my_file.json'
csv_filename = 'my_file.csv' def feature_to_row(feature, header): l = [] for k in header: l.append(feature['properties'][k]) coords = feature['geometry']['coordinates'] assert(len(coords)==2) l.extend(coords) return l with open(geo_filename, 'r') as geo_file: with open(csv_filename, 'w', newline='') as csv_file: geojson_data = json.load(geo_file) features = geojson_data['features'] csv_writer = csv.writer(csv_file) is_header = True header = [] for feature in features: if is_header: is_header = False header = list(feature['properties'].keys()) header.extend(['px','py']) csv_writer.writerow(header) csv_writer.writerow(feature_to_row(feature, feature['properties'].keys())) 

You can either copy&paste this code and run it in the same folder as your GeoJSON (of course, after renaming the input and output filenames.

Or you can check out this excellent GitHub to get a more “scriptable” variant to be used in the command line. This code is inspired by the GitHub but simplified significantly.

Example input:

{ "type": "FeatureCollection", "features": [ { "type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}, "properties": { "name": "Location A", "category": "Store" } }, { "type": "Feature", "geometry": {"type": "Point", "coordinates": [-80.24, 40.12]}, "properties": { "name": "Location B", "category": "House" } }, { "type": "Feature", "geometry": {"type": "Point", "coordinates": [ -77.2, 41.427]}, "properties": { "name": "Location C", "category": "Office" } } ] }

Example output:

GeoJSON to CSV in QGIS

In QGIS, if you have a map like this one (source):

You can convert GEOJSON to CSV right within QGIS by clicking Export, then Save Feature As and select the Comma Separated Value [CSV] selector in the first dropdown menu.

GeoJSON to CSV Online Converter

You can easily convert specific GeoJSON snippets to CSV using the following online converter:

Problem Formulation and Solution Overview

In this article, you’ll learn how to iterate over a 1D, 2D and 3D NumPy array using Python.

When working with the NumPy library, you will encounter situations where you will need to iterate through a 1D, 2D and even a 3D array. This article will show you how to accomplish this task.

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Question: How would we write code to iterate through a 1D, 2D or 3D NumPy array?

We can accomplish this task by one of the following options:

• Method 1: Use a For loop and np.array()
• Method 2: Use a For loop and np.nditer()
• Method 3: Use a For loop and itertools
• Method 4: Use a While loop and np.size
• Method 5: Use a For loop and np.ndenumerate()
• Method 6: Use a For Loop and range()
• Bonus: CSV to np.array()

---

Preparation

Before moving forward, please ensure the NumPy library is installed. Click here if you require instructions.

Then, add the following code to the top of each script. This snippet will allow the code in this article to run error-free.

import numpy as np 

After importing the NumPy library, this library is referenced by calling the shortcode (np).

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Method 1: Use a For Loop and np.array()

This method uses a For loop combined with np.array() to iterate through a 1D NumPy array. The first five (5) Atomic Numbers from the Periodic Table are generated and displayed for this example.

atomic_els = np.array(np.arange(1,6)) for el in atomic_els: print(el, end=' ')

Above, calls the np.array() function and passes it np.arange(1,6) with two (2) arguments: a start position of one (1) and stop position of five (5) or (stop-1). The result is an array of integers and saves to atomic_els.

[1 2 3 4 5]

Next, a For loop is instantiated and iterates through each element of the 1D NumPy array atomic_els.

The output is sent to the terminal on one (1) line as an additional argument was passed to the print statement (end=' '). This argument replaces the default newline character with a blank space.

1 2 3 4 5

---

Method 2: Uses a For Loop and nditer()

This method uses a for loop combined with np.nditer() to iterate through a NumPy array. The first five (5) Atomic Numbers and Number of Neutrons data from the Periodic Table display for this example.

atomic_data = np.array([np.arange(1,6), [0, 2, 4, 5, 6]]) for dim in atomic_data: for d in dim: print(d, end=' ')

Above, calls the np.array() function and passes np.arange(1,6) as the first argument and an array of associated Number of Neutrons values as the second.

Next, an outer for loop is instantiated. This iterates through each array dimension.

Inside this loop, another for loop is instantiated. The inside loop iterates through each element of the above dimensions.

The output is sent to the terminal on one (1) line as an additional argument is passed to the print statement (end=' '). This argument replaces the default newline character with a blank space.

1 2 3 4 5 0 2 4 5 6

Note: The first dimension is highlighted to easily differentiate the dimensions.

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Method 3: Use a For Loop and itertools

This method uses a for loop and Python’s built-in itertools library to iterate through a NumPy array. The first three (3) Atomic Numbers, Phase, and Group data from the Periodic Table display for this example.

import itertools atomic_num = np.arange(1,4)
atomic_phase = ['gas', 'gas', 'solid']
atomic_group = [1, 18, 1] for (a, b, c) in itertools.zip_longest(atomic_num, atomic_phase, atomic_group, fillvalue=0): print (a, b, c)

Above, imports the itertools library to use the zip_longest() function.

Then, three (3) arrays are declared containing the relevant data for the first three (3) elements from the Periodic Table. They save respectively to atomic_num, atomic_phase, and atomic_group.

Next, a for loop is instantiated and passed three (3) arguments inside the brackets (a, b, c) which reference the arguments inside the zip_longest(atomic_num, atomic_phase, atomic_group) function.

An additional argument (fillvalue=0) is also passed. This fills in any missing values for lists of unequal lengths with the stated value.

The loop iterates until the end of the arrays are reached, outputting to the terminal for each iteration.

1 gas 1 2 gas 18 3 solid 1

---

Method 4: Use a While Loop and Size

This method uses a while loop combined with np.size to iterate through a NumPy array. The first five (5) Atomic Numbers, Number of Neutrons, and Phase data from the Periodic Table display for this example.

atomic_names = np.array(['Hydrogen', 'Helium', 'Lithium', 'Beryllium', 'Boron'])
el_count = 0 while el_count < atomic_names.size: print(atomic_names[el_count], end=' ') el_count += 1

Above, calls the np.array() function and passes a list containing the first five (5) element names of the Periodic Table. These results save to atomic_names.

Then, a variable, el_count, is declared with a start value of zero (0). This will be a counter variable and lets the While loop know when to stop iterating.

Next, a while loop is instantiated and iterates through the elements of atomic_names. This will continue while the value of el_count remains less than the value of atomic_names.size.

The output is sent to the terminal on one (1) line as an additional argument is passed to the print statement (end=' '). This argument replaces the default newline character with a blank space.

Hydrogen Helium Lithium Beryllium Boron

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Method 5: Use a For Loop and np.ndenumerate()

This method uses a For loop and np.ndenumerate() to iterate through a NumPy array. The first five (5) Element Names from the Periodic Table display for this example.

atomic_names = np.array(['Hydrogen', 'Helium', 'Lithium', 'Beryllium', 'Boron']) for idx, x in np.ndenumerate(atomic_names): print(idx, x)

Above, calls the np.array() function and passes it an array containing the first five (5) element names from the Periodic Table. The results save to atomic_names.

Next, a for loop is instantiated referencing idx which is the index of the array, and x, which is the array element’s value.

The output is sent to the terminal. Then idx displays a Tuple containing the index value of the array and then the value of x for each iteration.

(0,) Hydrogen (1,) Helium (2,) Lithium (3,) Beryllium (4,) Boron

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Method 6: Use a For Loop and range()

This method uses a for loop and range()) to iterate through a 3D NumPy array.

nums = np.array([[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]], [[13, 14], [15, 16], [17, 18]], [[19, 20], [21, 22], [23, 23]], [[24, 25], [26, 27], [28, 29]]]) for x in range(0, 5): for y in range(0, 3): for z in range(0, 2): print(nums[x][y][z])

Above declares a 3D NumPy array containing consecutive numbers from 1-29 inclusive. These save to nums.

Next, three (3) for loops are instantiated to loop through and output the contents of nums to the terminal one (1) number per line.

The range() function for each loop is based on the dimensions of the 3D Numpy array, for example:

• The first loop (range(0,5) identifies the total number of tows in the array.

• The next loop (range(0,3) identifies the number of columns in the array.

• The final loop (range(0,2) identifies the elements in each column in the array.

Finally, the output is sent to the terminal (snippet only).

1 2 3 4 5 6 7 8 9 10 ...

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Bonus: Convert CSV to np.array()

This example reads in a snippet of the Periodic Table as a CSV file. This data is converted to a NumPy array and is output to the terminal.

The CSV file below contains the values of the Atomic Number, Atomic Mass, Number of Neutrons, and the Number of Electrons for the first seven (7) elements from the Periodic Table.

Contents of pt_sample.csv

1,1.007,0 2,4.002,2 3,6.941,4 4,9.012,5 5,10.811,6 6,12.011,6 7,14.007,7

To follow along, save this file as pt_sample.csv and move it into the current working directory.

Before moving forward, please ensure the Pandas library is installed. Click here if you require instructions.

import pandas as pd
import csv file_name = 'pt_sample.csv'
df_data = np.array(list(csv.reader(open(file_name, 'r'), delimiter=','))).astype("float")
print(df_data)

Above, imports the Pandas library and the CSV library. This is needed to work with DataFrames and read in the CSV file.

Then, a filename is declared and saves to file_name.

On the highlighted line, the np.array() function is called and passed the following arguments.

• The csv.reader() function is passed as an argument and this argument passes the open() method to open the specified CSV file in read (r) mode, including the field delimiter (csv.reader(open(file_name, 'r'), delimiter=',')).
• The contents of the CSV file converts to a list.
• This list converts to an np.array().

The results save to df_data and output to the terminal.

[[ 1. 1.007 0. ] [ 2. 4.002 2. ] [ 3. 6.941 4. ] [ 4. 9.012 5. ] [ 5. 10.811 6. ] [ 6. 12.011 6. ] [ 7. 14.007 7. ]]

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Summary

These seven (7) methods of iterating a NumPy array should give you enough information to select the best one for your coding requirements.

Good Luck & Happy Coding!

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Programmer Humor – Blockchain

Python Append Row to CSV

To append a row (=dictionary) to an existing CSV, open the file object in append mode using open('my_file.csv', 'a', newline=''). Then create a csv.DictWriter() to append a dict row using DictWriter.writerow(my_dict).

Given the following file 'my_file.csv':

You can append a row (dict) to the CSV file via this code snippet:

import csv # Create the dictionary (=row)
row = {'A':'Y1', 'B':'Y2', 'C':'Y3'} # Open the CSV file in "append" mode
with open('my_file.csv', 'a', newline='') as f: # Create a dictionary writer with the dict keys as column fieldnames writer = csv.DictWriter(f, fieldnames=row.keys()) # Append single row to CSV writer.writerow(row)

After running the code in the same folder as your original 'my_file.csv', you’ll see the following result:

Append Multiple Rows to CSV

Given the following CSV file:

To add multiple rows (i.e., dicts) to an old existing CSV file, iterate over the rows and write each row by calling csv.DictWriter.writerow(row) on the initially created DictWriter object.

Here’s an example (major changes highlighted):

import csv # Create the dictionary (=row)
rows = [{'A':'Z1', 'B':'Z2', 'C':'Z3'}, {'A':'ZZ1', 'B':'ZZ2', 'C':'ZZ3'}, {'A':'ZZZ1', 'B':'ZZZ2', 'C':'ZZZ3'}] # Open the CSV file in "append" mode
with open('my_file.csv', 'a', newline='') as f: # Create a dictionary writer with the dict keys as column fieldnames writer = csv.DictWriter(f, fieldnames=rows[0].keys()) # Append multiple rows to CSV for row in rows: writer.writerow(row)

The resulting CSV file has all three rows added to the first row:

Python Add Row to CSV Pandas

To add a row to an existing CSV using Pandas, you can set the write mode argument to append 'a' in the pandas DataFrame to_csv() method like so: df.to_csv('my_csv.csv', mode='a', header=False).

df.to_csv('my_csv.csv', mode='a', header=False)

For a full example, check out this code snippet:

import pandas as pd # Create the initial CSV data
rows = [{'A':'Z1', 'B':'Z2', 'C':'Z3'}, {'A':'ZZ1', 'B':'ZZ2', 'C':'ZZ3'}, {'A':'ZZZ1', 'B':'ZZZ2', 'C':'ZZZ3'}] # Create a DataFrame and write to CSV
df = pd.DataFrame(rows)
df.to_csv('my_file.csv', header=False, index=False) # Create another row and append row (as df) to existing CSV
row = [{'A':'X1', 'B':'X2', 'C':'X3'}]
df = pd.DataFrame(row)
df.to_csv('my_file.csv', mode='a', header=False, index=False)

The output file looks like this (new row highlighted):

Alternatively, you can open the file in append mode using normal open() function with the append 'a' argument and pass it into the pandas DataFrame to_csv() method.

Here’s an example snippet for copy&paste:

with open('my_csv.csv', 'a') as f: df.to_csv(f, header=False)

Learn More: If you want to learn about 7 Best Ways to Convert Dict to CSV in Python, check out this Finxter tutorial.

Question: How to convert a dictionary to a CSV in Python?

In Python, convert a dictionary to a CSV file using the DictWriter() method from the csv module. The csv.DictWriter() method allows you to insert a dictionary-formatted row (keys=column names; values=row elements) into the CSV file using its DictWriter.writerow() method.

Learn More: If you want to learn about converting a list of dictionaries to a CSV, check out this Finxter tutorial.

Method 1: Python Dict to CSV in Pandas

First, convert a list of dictionaries to a Pandas DataFrame that provides you with powerful capabilities such as the

Second, convert the Pandas DataFrame to a CSV file using the DataFrame’s to_csv() method with the filename as argument.

salary = [{'Name':'Alice', 'Job':'Data Scientist', 'Salary':122000}, {'Name':'Bob', 'Job':'Engineer', 'Salary':77000}, {'Name':'Carl', 'Job':'Manager', 'Salary':119000}] # Method 1
import pandas as pd
df = pd.DataFrame(salary)
df.to_csv('my_file.csv', index=False, header=True)

Output:

Name,Job,Salary
Alice,Data Scientist,122000
Bob,Engineer,77000
Carl,Manager,119000

You create a Pandas DataFrame—which is Python’s default representation of tabular data. Think of it as an Excel spreadsheet within your code (with rows and columns).

The DataFrame is a very powerful data structure that allows you to perform various methods. One of those is the to_csv() method that allows you to write its contents into a CSV file.

• You set the index argument of the to_csv() method to False because Pandas, per default, adds integer row and column indices 0, 1, 2, …. Again, think of them as the row and column indices in your Excel spreadsheet. You don’t want them to appear in the CSV file so you set the arguments to False.
• You set the and header argument to True because you want the dict keys to be used as headers of the CSV.

If you want to customize the CSV output, you’ve got a lot of special arguments to play with. Check out this Finxter Tutorial for a comprehensive list of all arguments.

Related article: Pandas Cheat Sheets to Pin to Your Wall

Method 2: Dict to CSV File

In Python, convert a dictionary to a CSV file using the DictWriter() method from the csv module. The csv.DictWriter() method allows you to insert data into the CSV file using its DictWriter.writerow() method.

The following example writes the dictionary to a CSV using the keys as column names and the values as row values.

import csv data = {'A':'X1', 'B':'X2', 'C':'X3'} with open('my_file.csv', 'w', newline='') as f: writer = csv.DictWriter(f, fieldnames=data.keys()) writer.writeheader() writer.writerow(data)

The resulting file 'my_file.csv' looks like this:

The csv library may not yet installed on your machine. To check if it is installed, follow these instructions. If it is not installed, fix it by running pip install csv in your shell or terminal.

Method 3: Dict to CSV String (in Memory)

To convert a list of dicts to a CSV string in memory, i.e., returning a CSV string instead of writing in a CSV file, use the pandas.DataFrame.to_csv() function without file path argument. The return value is a CSV string representation of the dictionary.

Here’s an example:

import pandas as pd data = [{'A':'X1', 'B':'X2', 'C':'X3'}, {'A':'Y1', 'B':'Y2', 'C':'Y3'}] df = pd.DataFrame(data)
my_csv_string = df.to_csv(index=False) print(my_csv_string) '''
A,B,C
X1,X2,X3
Y1,Y2,Y3 '''

We pass index=False because we don’t want an index 0, 1, 2 in front of each row.

Method 4: Dict to CSV Append Line

To append a dictionary to an existing CSV, you can open the file object in append mode using open('my_file.csv', 'a', newline='') and using the csv.DictWriter() to append a dict row using DictWriter.writerow(my_dict).

Given the following file 'my_file.csv':

You can append a row (dict) to the CSV file via this code snippet:

import csv row = {'A':'Y1', 'B':'Y2', 'C':'Y3'} with open('my_file.csv', 'a', newline='') as f: writer = csv.DictWriter(f, fieldnames=row.keys()) writer.writerow(row)

After running the code in the same folder as your original 'my_file.csv', you’ll see the following result:

Method 5: Dict to CSV Columns

To write a Python dictionary in a CSV file as a column, i.e., a single (key, value) pair per row, use the following three steps:

• Open the file in writing mode and using the newline='' argument to prevent blank lines.
• Create a CSV writer object.
• Iterate over the (key, value) pairs of the dictionary using the dict.items() method.
• Write one (key, value) tuple at a time by passing it in the writer.writerow() method.

Here’s the code example:

import csv data = {'A':42, 'B':41, 'C':40} with open('my_file.csv', 'w', newline='') as f: writer = csv.writer(f) for row in data.items(): writer.writerow(row)

Your output CSV file (column dict) looks like this:

Method 6: Dict to CSV with Header

Convert a Python dictionary to a CSV file with header using the csv.DictWriter(fileobject, fieldnames) method to create a writer object used for writing the header via writer.writeheader() without argument. This writes the list of column names passed as fieldnames, e.g., the dictionary keys obtained via dict.keys().

To write the rows, you can then call the DictWriter.writerow() method.

The following example writes the dictionary to a CSV using the keys as column names and the values as row values.

import csv data = {'A':'X1', 'B':'X2', 'C':'X3'} with open('my_file.csv', 'w', newline='') as f: writer = csv.DictWriter(f, fieldnames=data.keys()) writer.writeheader() writer.writerow(data)

The resulting file 'my_file.csv' looks like this:

Where to Go From Here

If you haven’t found your solution, yet, you may want to check out my in-depth guide on how to write a list of dicts to a CSV:

Guide: Write List of Dicts to CSV in Python

Bonus Method 7: Vanilla Python

If you don’t want to import any library and still convert a list of dicts into a CSV file, you can use standard Python implementation as well: it’s not complicated and very efficient.

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This method is best if you won’t or cannot use external dependencies.

• Open the file f in writing mode using the standard open() function.
• Write the first dictionary’s keys in the file using the one-liner expression f.write(','.join(salary[0].keys())).
• Iterate over the list of dicts and write the values in the CSV using the expression f.write(','.join(str(x) for x in row.values())).

Here’s the concrete code example:

salary = [{'Name':'Alice', 'Job':'Data Scientist', 'Salary':122000}, {'Name':'Bob', 'Job':'Engineer', 'Salary':77000}, {'Name':'Carl', 'Job':'Manager', 'Salary':119000}] # Method 3
with open('my_file.csv','w') as f: f.write(','.join(salary[0].keys())) f.write('n') for row in salary: f.write(','.join(str(x) for x in row.values())) f.write('n')

Output:

Name,Job,Salary
Alice,Data Scientist,122000
Bob,Engineer,77000
Carl,Manager,119000

In the code, you first open the file object f. Then you iterate over each row and each element in the row and write the element to the file—one by one. After each element, you place the comma to generate the CSV file format. After each row, you place the newline character 'n'.

Note: to get rid of the trailing comma, you can check if the element x is the last element in the row within the loop body and skip writing the comma if it is.

Related Tutorial: How to Convert a List to a CSV File in Python [5 Ways]

Programming Humor – Python

Where to Go From Here?

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