Mapping Geograph Data in Python (2022)

Mapping Geograph Data in Python (1)

One great help when working in Data Science, is to visualize your data on a geo map and for that, several packages can take care of it, as GeoPandas for example.

You can learn how to use GeoPandas, reading my article: How Safe are the Streets of Santiago.

Sometimes install Geopandas packages can be complicated, depending on what environment you are working. Or, simplesly you need take the control seat of your code! So, on this article we will explore on "the hard way", how to construct our own "geo map functions", using "Shapefiles" and basic Python libraries.

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1. Shapefiles

Developed and regulated by Esri as a (mostly) open specification, the shapefile format spatially describes geometries as either ‘points’, ‘polylines’, or ‘polygons’. In OpenStreetMap terms these can be considered as ‘nodes’, ‘ways’ and ‘closed ways’, respectively. Each geometry has a set of associated attributes. Broadly speaking these are a bit like OSM’s tags.

The shapefile is in fact a grouping of several files formatted to represent different aspects of geodata:

  • .shp — shape format; the feature geometry itself.
  • .shx — shape index format; a positional index of the feature geometry to allow seeking forwards and backwards quickly.
  • .dbf — attribute format; columnar attributes for each shape, in dBase IV format.

There are also several optional files in the shapefile format. The most significant of these is the .prj file which describes the coordinate system and projection information used. Although not part of the Esri shapefile standard, the .lyr file is often included as it contains specifications of how to display the data (colour, labelling, etc) in ArcGIS software.

For more info see wikipedia

2. Installing Python Shapefile Library (PyShp)

The Python Shapefile Library (pyshp) provides read and write support for the
Esri Shapefile format. The Shapefile format is a popular Geographic
Information System vector data format created by Esri.

To Install pyshp, execute below instruction in your Terminal:

pip install pyshp

3. Importing and initializing main Python libraries

import numpy as np
import pandas as pd
import shapefile as shp
import matplotlib.pyplot as plt
import seaborn as sns

Initializing vizualization set

sns.set(style=”whitegrid”, palette=”pastel”, color_codes=True)
sns.mpl.rc(“figure”, figsize=(10,6))

and if you are using a Jupyter Notebook:

%matplotlib inline

4. Opening a Vector Map

As described on 1., a vector map is a group of several files, with name.shp being the main one, where the geographic features are saved. Important that all other files as 'name.shx', ' name.dbf', etc., must be at same folder.

On this tutorial, we will work with maps related to the cities ("Comunas") that together, make the Santiago Metropolitan Region. On INE (Chilean National Institute of Statistics), is possible to download a group of shapefiles related with maps, created for the last national 2017 census :

  • Comuna.cpg
  • Comuna.shp
  • Comuna.dbf
  • Comuna.shp.xml
  • Comuna.prj
  • Comuna.shx
  • Comuna.sbn
  • Comuna.sbx
shp_path = “./Comunas_RM_Mapas_Vectoriales/Comuna.shp”
sf = shp.Reader(shp_path)

Let's check how many different "shapes" were imported by our function shp.Reader:

len(sf.shapes())

The result will be: 52

This means that exist 52 shapes on our shape files, what make sense, once the Santiago Metropolitan Region has 52 "comunas" as shown at below map (do not worry, before the end of this article, you will learn how to create a map like this one, directly from your data):

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Let's also explore one of the shapes (or "records"):

(Video) Plotting Choropleth Maps using Python (Plotly)

sf.records()[1]

The result will be an array with 6 elements:

Out:['13',
'131',
'13115',
'REGIÓN METROPOLITANA DE SANTIAGO',
'SANTIAGO',
'LO BARNECHEA']

The element [5] is the name of the 'comuna', in this case: 'LO BARNECHEA', a 'comuna' located on oriental part of the city, where the Andes montains are located (and also my home! ;-)

You can get its name directlly:

sf.records()[1][5]

The most central 'comuna' of Santiago Metropolitan Region is exactly the Comuna of Santiago (little confuse?), where you can find the Metropolitan Catedral, La Moneda Presidential Palace (That was heavy bombed on 73), Pablo Neruda's house, etc.

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But, Let’ s end sightseeing and take a look on Santiago's comuna data structure (id: 25):

sf.records()[25]

Out[]:

[‘13’,
‘131’,
‘13101’,
‘REGIÓN METROPOLITANA DE SANTIAGO’,
‘SANTIAGO’,
‘SANTIAGO’]

We can see that some data changed and most importantly, the name of the 'comuna', that is now, 'SANTIAGO'.

Note that you can apply what will be describe on this tutorial to any shapfile.

5. Converting shapefile data on Pandas dataframe

In the last example, I previously knew that Santiago'’ id was '25'. But how to find such id, starting from a comuna's name? Let's first create a usefull function to convert our 'shapefile' format on a more commun Pandas dataframe format:

def read_shapefile(sf):
"""
Read a shapefile into a Pandas dataframe with a 'coords'
column holding the geometry information. This uses the pyshp
package
"""
fields = [x[0] for x in sf.fields][1:]
records = sf.records()
shps = [s.points for s in sf.shapes()]
df = pd.DataFrame(columns=fields, data=records)
df = df.assign(coords=shps)
return df

So, let's convert sf data on a dataframe and see how it looks like:

df = read_shapefile(sf)
df.shape

The dataframe has a shape of (52, 7). What means that we we have 7 diferent features (columns) for each line ('comuna'). Remember that previosly we saw 6 of those features. Seems that an extra one was added now. Let's see a sample:

df.sample(5)

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The last column is exactelly the coordinates, latitude and longitude, of every point that was used to create a specific map shape.

Confuse? Let's dig a little bit more.

How we can locate the Santiago comuna's id? Now with Pandas is very simple:

df[df.NOM_COMUNA == ‘SANTIAGO’]

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We can easily see that 25 is exactelly the dataframe index, where our comuna shape is located.

With simple Pandas' s commands you can related the index (or id) with the comuna's name:

df.NOM_COMUNAOut:0 LAS CONDES
1 LO BARNECHEA
2 VITACURA
3 HUECHURABA
4 PUDAHUEL
49 ALHUÉ
50 LAMPA
51 TILTIL

6. Plotting a specific shape

Finally, we will see what a shape really is. For that, we should create a function to plot it. We will use the Python MatPlotLib library:

def plot_shape(id, s=None):
""" PLOTS A SINGLE SHAPE """
plt.figure()
ax = plt.axes()
ax.set_aspect('equal')
shape_ex = sf.shape(id)
x_lon = np.zeros((len(shape_ex.points),1))
y_lat = np.zeros((len(shape_ex.points),1))
for ip in range(len(shape_ex.points)):
x_lon[ip] = shape_ex.points[ip][0]
y_lat[ip] = shape_ex.points[ip][1]
plt.plot(x_lon,y_lat)
x0 = np.mean(x_lon)
y0 = np.mean(y_lat)
plt.text(x0, y0, s, fontsize=10)
# use bbox (bounding box) to set plot limits
plt.xlim(shape_ex.bbox[0],shape_ex.bbox[2])
return x0, y0

The above function does two things: a) Plot the shape (polygon) based on the comuna's coordinates and, b) calculate and return the medium point of that specific shape (x0, y0). This medium point was also used to define where to print the comuna's name.

For example, for our famous Santiago's comuna:

(Video) How to Plot Data on an Interactive Geographical Map in Python Easily with Geopy and Folium

comuna = 'SANTIAGO'
com_id = df[df.NOM_COMUNA == comuna].index.get_values()[0]
plot_shape(com_id, comuna)

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Note that we must know the shape id (index) to plot it, but we entered with the Comuna's name: SANTIAGO. Using Pandas was ease to calculate the id as you can see on the second line of the previous code.

7. Plotting a complete map

Plotting a single shape was basically to work around this small part of the code:

sf.shape(id)

Now, we must plot at same picture, all the shapes that are on our dataframe. For that, we will use the following function:

def plot_map(sf, x_lim = None, y_lim = None, figsize = (11,9)):
'''
Plot map with lim coordinates
'''
plt.figure(figsize = figsize)
id=0
for shape in sf.shapeRecords():
x = [i[0] for i in shape.shape.points[:]]
y = [i[1] for i in shape.shape.points[:]]
plt.plot(x, y, 'k')

if (x_lim == None) & (y_lim == None):
x0 = np.mean(x)
y0 = np.mean(y)
plt.text(x0, y0, id, fontsize=10)
id = id+1

if (x_lim != None) & (y_lim != None):
plt.xlim(x_lim)
plt.ylim(y_lim)

The above function, by default, plot all shapes on a given 'df' file, including its shape id at middle of it. Or a zoomed map will be plotted (w/o ids). You can change the function to print or not the ids.

Plotting a full map:

plot_map(sf)

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Plotting a zommed map:

y_lim = (-33.7,-33.3) # latitude 
x_lim = (-71, -70.25) # longitude
plot_map(sf, x_lim, y_lim)

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8. Plotting a single shape over a complete map

We can "merge" the two previous functions and "plot" a single shape inside a full map. For that, let's write a new function, where the shape id is now an input parameter:

def plot_map2(id, sf, x_lim = None, y_lim = None, figsize=(11,9)):
'''
Plot map with lim coordinates
'''

plt.figure(figsize = figsize)
for shape in sf.shapeRecords():
x = [i[0] for i in shape.shape.points[:]]
y = [i[1] for i in shape.shape.points[:]]
plt.plot(x, y, 'k')

shape_ex = sf.shape(id)
x_lon = np.zeros((len(shape_ex.points),1))
y_lat = np.zeros((len(shape_ex.points),1))
for ip in range(len(shape_ex.points)):
x_lon[ip] = shape_ex.points[ip][0]
y_lat[ip] = shape_ex.points[ip][1]
plt.plot(x_lon,y_lat, 'r', linewidth=3)

if (x_lim != None) & (y_lim != None):
plt.xlim(x_lim)
plt.ylim(y_lim)

Plotting the Santiago's comuna in "red":

plot_map2(25, sf, x_lim, y_lim)

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And if we want to "fill" a single shape with a specific color? Simple! We can use plt.fill for that. The function can be rewriten:

def plot_map_fill(id, sf, x_lim = None, 
y_lim = None,
figsize = (11,9),
color = 'r'):
'''
Plot map with lim coordinates
'''

plt.figure(figsize = figsize)
fig, ax = plt.subplots(figsize = figsize)

for shape in sf.shapeRecords():
x = [i[0] for i in shape.shape.points[:]]
y = [i[1] for i in shape.shape.points[:]]
ax.plot(x, y, 'k')

shape_ex = sf.shape(id)
x_lon = np.zeros((len(shape_ex.points),1))
y_lat = np.zeros((len(shape_ex.points),1))
for ip in range(len(shape_ex.points)):
x_lon[ip] = shape_ex.points[ip][0]
y_lat[ip] = shape_ex.points[ip][1]
ax.fill(x_lon,y_lat, color)

if (x_lim != None) & (y_lim != None):
plt.xlim(x_lim)
plt.ylim(y_lim)

Plotting the comuna of "Las Condes" (id=0) in green ('g'):

plot_map_fill(0, sf, x_lim, y_lim, color='g')

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(Video) How to plot geographic location in Python | Choropleth map

9. Plotting multiple shapes on a full map

The next natural step on our "hard way mapping journey", will be create a map where several shapes are selected. For that, insteady of having an id as input parameter, we will have a list of ids, and will use a for loop to fill with color each one of them. The modified function is shown below:

def plot_map_fill_multiples_ids(title, comuna, sf, 
x_lim = None,
y_lim = None,
figsize = (11,9),
color = 'r'):
'''
Plot map with lim coordinates
'''

plt.figure(figsize = figsize)
fig, ax = plt.subplots(figsize = figsize)
fig.suptitle(title, fontsize=16)

for shape in sf.shapeRecords():
x = [i[0] for i in shape.shape.points[:]]
y = [i[1] for i in shape.shape.points[:]]
ax.plot(x, y, 'k')

for id in comuna:
shape_ex = sf.shape(id)
x_lon = np.zeros((len(shape_ex.points),1))
y_lat = np.zeros((len(shape_ex.points),1))
for ip in range(len(shape_ex.points)):
x_lon[ip] = shape_ex.points[ip][0]
y_lat[ip] = shape_ex.points[ip][1]
ax.fill(x_lon,y_lat, color)

x0 = np.mean(x_lon)
y0 = np.mean(y_lat)
plt.text(x0, y0, id, fontsize=10)

if (x_lim != None) & (y_lim != None):
plt.xlim(x_lim)
plt.ylim(y_lim)

On the above function, "comuna" is now a list of ids:

comuna_id = [0, 1, 2, 3, 4, 5, 6]
plot_map_fill_multiples_ids("Multiple Shapes",
comuna_id, sf, color = 'r')

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Taking opportunity of our previous pandas dataframe, let's create a simple function where the input is the name of comuna, insteady of its id:

def plot_comunas_2(sf, title, comunas, color):
'''
Plot map with selected comunes, using specific color
'''

df = read_shapefile(sf)
comuna_id = []
for i in comunas:
comuna_id.append(df[df.NOM_COMUNA == i.upper()]
.index.get_values()[0])
plot_map_fill_multiples_ids(title, comuna_id, sf,
x_lim = None,
y_lim = None,
figsize = (11,9),
color = color);

Plotting the southern comunes of Santiago Metropolitan Region:

south = ['alhué', 'calera de tango', 'buin', 'isla de maipo', 'el bosque', 'paine', 'la granja', 'pedro aguirre cerda', 'lo espejo', 'puente alto', 'san joaquín', 'san miguel', 'pirque', 'san bernardo', 'san ramón', 'la cisterna', 'talagante', 'la pintana']plot_comunas_2(sf, 'South', south, 'c')

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10. Creating 'Heat Maps'

A very useful type of map is to fill a specific shape with a color, which "intensity" is proportional to a given value. Doing that, is possible to have a general overview about data distribution on a specific geographic area. For example, population distibution.

First we will create a function that once receiving a list of data, will split them on "bins". For each one of those beans will get a specific color assigned. For experience, usually 5 to 7 bins are good to have a good feeling of data distribution. We will use 6 bins and 4 different color palettes associated with those bins. You must select one of those bins at time.

def calc_color(data, color=None):
if color == 1: color_sq =
['#dadaebFF','#bcbddcF0','#9e9ac8F0',
'#807dbaF0','#6a51a3F0','#54278fF0'];
colors = 'Purples';
elif color == 2: color_sq =
['#c7e9b4','#7fcdbb','#41b6c4',
'#1d91c0','#225ea8','#253494'];
colors = 'YlGnBu';
elif color == 3: color_sq =
['#f7f7f7','#d9d9d9','#bdbdbd',
'#969696','#636363','#252525'];
colors = 'Greys';
elif color == 9: color_sq =
['#ff0000','#ff0000','#ff0000',
'#ff0000','#ff0000','#ff0000']
else: color_sq =
['#ffffd4','#fee391','#fec44f',
'#fe9929','#d95f0e','#993404'];
colors = 'YlOrBr';
new_data, bins = pd.qcut(data, 6, retbins=True,
labels=list(range(6)))
color_ton = []
for val in new_data:
color_ton.append(color_sq[val])
if color != 9:
colors = sns.color_palette(colors, n_colors=6)
sns.palplot(colors, 0.6);
for i in range(6):
print ("\n"+str(i+1)+': '+str(int(bins[i]))+
" => "+str(int(bins[i+1])-1), end =" ")
print("\n\n 1 2 3 4 5 6")
return color_ton, bins;

Both functions plot_comunas() and plot_map_fill_multiples_ids should be adapted to take advantage of this new colored scheme:

def plot_comunas_data(sf, title, comunas, data=None, 
color=None, print_id=False):
'''
Plot map with selected comunes, using specific color
'''

color_ton, bins = calc_color(data, color)
df = read_shapefile(sf)
comuna_id = []
for i in comunas:
i = conv_comuna(i).upper()
comuna_id.append(df[df.NOM_COMUNA ==
i.upper()].index.get_values()[0])
plot_map_fill_multiples_ids_tone(sf, title, comuna_id,
print_id,
color_ton,
bins,
x_lim = None,
y_lim = None,
figsize = (11,9));

and,

def plot_map_fill_multiples_ids_tone(sf, title, comuna, 
print_id, color_ton,
bins,
x_lim = None,
y_lim = None,
figsize = (11,9)):
'''
Plot map with lim coordinates
'''

plt.figure(figsize = figsize)
fig, ax = plt.subplots(figsize = figsize)
fig.suptitle(title, fontsize=16)

for shape in sf.shapeRecords():
x = [i[0] for i in shape.shape.points[:]]
y = [i[1] for i in shape.shape.points[:]]
ax.plot(x, y, 'k')

for id in comuna:
shape_ex = sf.shape(id)
x_lon = np.zeros((len(shape_ex.points),1))
y_lat = np.zeros((len(shape_ex.points),1))
for ip in range(len(shape_ex.points)):
x_lon[ip] = shape_ex.points[ip][0]
y_lat[ip] = shape_ex.points[ip][1]
ax.fill(x_lon,y_lat, color_ton[comuna.index(id)])
if print_id != False:
x0 = np.mean(x_lon)
y0 = np.mean(y_lat)
plt.text(x0, y0, id, fontsize=10)
if (x_lim != None) & (y_lim != None):
plt.xlim(x_lim)
plt.ylim(y_lim)

In order to test our new functions, let's take the previous list of shapes for the southern region of Santiago, associating a general value for each one of them. We will use the "color pallete #1 ('Purples'):

south = ['alhué', 'calera de tango', 'buin', 'isla de maipo', 'el bosque', 'paine', 'la granja', 'pedro aguirre cerda', 'lo espejo', 'puente alto', 'san joaquín', 'san miguel', 'pirque', 'san bernardo', 'san ramón', 'la cisterna', 'talagante', 'la pintana']data = [100, 2000, 300, 400000, 500, 600, 100, 2000, 300, 400, 500, 600, 100, 2000, 300, 400, 500, 600]print_id = True # The shape id will be printed
color_pallete = 1 # 'Purples'
plot_comunas_data(sf, 'South', south, data, color_pallete, print_id)

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Cool! Isn't ? ;-)

11. Plotting real data

To finish our overview regarding how to handle maps using Python, let's take some real data from last Chilean Census 2017 and apply those functions developed on part 10.

Reading dataset:

(Video) [46] Geospatial Data and Maps with Python (Christy Heaton)

census_17 = pd.read_excel('./data/CENSO_2017_COMUNAS_RM.xlsx')
census_17.shape
Out:
(52,7)

Our dataset has 52 lines, what make sense once each line contains data related to each one of Santiago's comunas.

Taking a look at dataset:

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The column "PERSONAS" for example is related to the number of persons that live on that especific comuna. "TOTAL_VIV" is the total number of homes on that comuna and so on.

Plotting:

Let's apply our Map functions to analyze how the population is distributed on Santiago Metropolitan area.

title = 'Population Distrubution on Santiago Metropolitan Region'
data = census_17.PERSONAS
names = census_17.NOM_COMUNA
plot_comunas_data(sf, title, names, data, 4, True)

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Great! We can see that population is heavily distributed around the center region of Metropolitan area! To the east (right on map), the population are sparce, what is logical, because this is the great Andes Montains! To the west and south are agricultural regions.

One more! Let's plot the percentual of immigrants over total population at Metropolitan Region of Santiago:

title = 'Percentual of immigrants over total population'
data = census_17.INM_PERC
names = census_17.NOM_COMUNA
plot_comunas_data(sf, title, names, data, 2, True)

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12. Conclusion

You can realize that at end you can create a map with only one line of code, using the 3 funcions developed on this article:

plot_comunas_data() .... that calls:plot_map_fill_multiples_ids_tone() ... that calls:calc_color()

What was developed on this article for Santiago Matropolitan area, can be easily adapted to be used with any vectorial map available at internet!

For example, you can go to US Census Bureau and download the Cartographic Boundary Shapefiles for US-States. Following what was done for Santiago,

shp_path = "./cb_2017_us_state_5m/cb_2017_us_state_5m.shp"
sf = shp.Reader(shp_path)
# Continental US
y_lim = (23, 50) # lat
x_lim = (-128, -65) # long
state_id = [0, 10, 3, 5, 6, 7, 8, 30]
plot_map_fill_multiples_ids("US - States", state_id, sf, x_lim,
y_lim, color = 'r', figsize = (15,9))

you can plot:

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That's all folks!

Hope you have learned more about the fantastic world of Data Science!

The Jupyter Notebook and all data used on this article can be downloaded from my GitHub.

See you on my next article!

Saludos from the south of the world!

(Video) plotting maps with geopandas and matplotlib

Marcelo

FAQs

How do you plot geo data in Python? ›

Following steps were followed:
  1. Define the x-axis and corresponding y-axis values as lists.
  2. Plot them on canvas using . plot() function.
  3. Give a name to x-axis and y-axis using . xlabel() and . ylabel() functions.
  4. Give a title to your plot using . title() function.
  5. Finally, to view your plot, we use . show() function.
15 Jul 2022

What is mapping data in Python? ›

Python's map() is a built-in function that allows you to process and transform all the items in an iterable without using an explicit for loop, a technique commonly known as mapping. map() is useful when you need to apply a transformation function to each item in an iterable and transform them into a new iterable.

How do you use mapping in Python? ›

Map in Python is a function that works as an iterator to return a result after applying a function to every item of an iterable (tuple, lists, etc.). It is used when you want to apply a single transformation function to all the iterable elements. The iterable and function are passed as arguments to the map in Python.

How do I make a chart map in Python? ›

Follow this link to know more.
  1. Importing libraries. In [1]: import pandas as pd import geopandas as gpd import matplotlib.pyplot as plt.
  2. Reading data file. In [2]: df = pd. ...
  3. Reading shape file. In [3]: shp_gdf = gpd. ...
  4. Merging data file and shape file based on names of Indian states. In [4]: ...
  5. Plotting map of India.

Which is better Seaborn or matplotlib? ›

Seaborn is more comfortable in handling Pandas data frames. It uses basic sets of methods to provide beautiful graphics in python. Matplotlib works efficiently with data frames and arrays.It treats figures and axes as objects. It contains various stateful APIs for plotting.

Is Plotly better than matplotlib? ›

Plotly has several advantages over matplotlib. One of the main advantages is that only a few lines of codes are necessary to create aesthetically pleasing, interactive plots. The interactivity also offers a number of advantages over static matplotlib plots: Saves time when initially exploring your dataset.

What is a mapping class in Python? ›

A mapping is a utility class that defines how data is mapped between objects, these objects are typically odin. Resource but other Python objects can be supported. The basics: Each mapping is a Python class that subclasses odin.

What is a map data structure? ›

• A Map is an abstract data structure (ADT) • it stores key-value (k,v) pairs. • there cannot be duplicate keys. • Maps are useful in situations where a key can be viewed as a unique identifier for the object. • the key is used to decide where to store the object in the structure.

What is __ init __ in Python? ›

The __init__ method is the Python equivalent of the C++ constructor in an object-oriented approach. The __init__ function is called every time an object is created from a class. The __init__ method lets the class initialize the object's attributes and serves no other purpose. It is only used within classes.

What does map () return in Python? ›

map() function returns a map object(which is an iterator) of the results after applying the given function to each item of a given iterable (list, tuple etc.)

What is programming mapping? ›

In many programming languages, map is the name of a higher-order function that applies a given function to each element of a collection, e.g. a list or set, returning the results in a collection of the same type. It is often called apply-to-all when considered in functional form.

How do I map a list to another list in Python? ›

The map() function iterates over all elements in a list (or a tuple), applies a function to each, and returns a new iterator of the new elements. In this syntax, fn is the name of the function that will call on each element of the list. In fact, you can pass any iterable to the map() function, not just a list or tuple.

How do you plot data on a map? ›

Plotting data on maps in R using ggmap - YouTube

How do you plot a map? ›

How to Plot on Google Maps
  1. Go to maps.google.com and click on the link for "My Maps." ...
  2. Click on the link for "Create New Map."
  3. Click on the blue placemarker icon in the upper left hand corner of the map. ...
  4. Move the cursor to the location that you want to add to the map.

How do you plot coordinates on a map? ›

Get the coordinates of a place
  1. On your computer, open Google Maps.
  2. Right-click the place or area on the map. This will open a pop-up window. You can find your latitude and longitude in decimal format at the top.
  3. To copy the coordinates automatically, left click on the latitude and longitude.

Why do people use Seaborn? ›

Seaborn is a high-level library. It provides simple codes to visualize complex statistical plots, which also happen to be aesthetically pleasing. But Seaborn was built on top of Matplotlib, meaning it can be further powered up with Matplotlib functionalities.

Is matplotlib difficult? ›

Once you know some basic Python, it isn't difficult to get started with Matplotlib. In fact, you can use Matplotlib as your first Python library if you want. The more Python you know, the more complex visualizations you can make. This is especially true if you also know how to use other tools like Pandas and Jupyter.

Does Seaborn use pandas? ›

Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures.

Is Python Plotly free? ›

Yes. Plotly's Dash analytics application framework is also free and open-source software, licensed under the MIT license.

What is better than Plotly? ›

This is obvious, but Matplotlib is way more popular than Plotly. The main advantage of being so popular is that notebooks using Matplotlib will be easily reproduced by other people since different people's chances of having it installed are higher.

Which Python library is used for data visualization? ›

Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community.

What is a mapped class? ›

A class mapping describes how a class maps to the database. It typically controls the primary table for the class and how the class is linked to its superclass data, if any. For classes using datastore identity, the class mapping also manages the primary key column for the class.

Is map faster than for loop? ›

Comparing performance , map() wins! map() works way faster than for loop. Considering the same code above when run in this ide.

What is the difference between map and dictionary in Python? ›

To answer the question in the title, it is the same. A map seen as a datastructure is the same concept as a dict . dict s also use hashes to map keys to values. That's why java developers call it hashmap.

What are map data types? ›

The map data type is known as an associative array because, like an array, it is a collection of values and not a single value like an Int or a String. Also, each unique key is associated with a value, making it an associative array.

What is the difference between map and array? ›

Map stores data in form of key : value which provides faster look ups as compared to an array. Map should be used when we have to maintain some relation between elements. eg : Count the occurrence of all the character in a given string and print key value pair for them.

What is data and map study? ›

Data mapping is the process of connecting a data field from one source to a data field in another source. This reduces the potential for errors, helps standardize your data, and makes it easier to understand your data by correlating it, for example, with identities.

What is __ init __( self? ›

While creating a person, “Nikhil” is passed as an argument, this argument will be passed to the __init__ method to initialize the object. The keyword self represents the instance of a class and binds the attributes with the given arguments.

What is main () in Python? ›

Some programming languages have a special function called main() which is the execution point for a program file. Python interpreter, however, runs each line serially from the top of the file and has no explicit main() function. Python offers other conventions to define the execution point.

Why do I need __ init __ PY? ›

The __init__.py files are required to make Python treat directories containing the file as packages. This prevents directories with a common name, such as string , unintentionally hiding valid modules that occur later on the module search path.

How do you use maps? ›

Start or stop navigation
  1. On your Android phone or tablet, open the Google Maps app .
  2. Search for a place or tap it on the map.
  3. At the bottom left, tap Directions. ...
  4. Choose your mode of transportation.
  5. If other routes are available, they'll show in gray on the map. ...
  6. To start navigation, tap Start.

What is map and filter in Python? ›

Map, Filter, and Reduce are paradigms of functional programming. They allow the programmer (you) to write simpler, shorter code, without neccessarily needing to bother about intricacies like loops and branching.

How do I convert a map to a string in Python? ›

“convert map to string python” Code Answer
  1. nums = ['3','4','7']
  2. nums = list(map(int, nums))
  3. print(nums)

Where is data mapping used? ›

Data mapping is essential for any company that processes data. It's mainly used to integrate data, build data warehouses, transform data, or migrate data from one place to another. The process of matching data to a schema is a fundamental part of the flow of data through any organization.

What is the purpose of mapping? ›

Maps present information about the world in a simple, visual way. They teach about the world by showing sizes and shapes of countries, locations of features, and distances between places.

What are the example of mapping? ›

An example of mapping is creating a map to get to your house. An example of mapping is identifying which cell on one spreadsheet contains the same information as the cell on another speadsheet. (mathematics) A function that maps every element of a given set to a unique element of another set; a correspondence.

Can I map a list in Python? ›

In Python, you can use map() to apply built-in functions, lambda expressions ( lambda ), functions defined with def , etc., to all items of iterables such as lists and tuples. This article describes the following contents. Note that map() can be substituted by list comprehensions or generator expressions.

How do you map 2 lists in Python? ›

To map two lists together, we can use the Python zip() function. This function allows us to combine two lists together. We can use one list as the keys for the dictionary and the other as the values. If the lists vary in size, this method will truncate the longer list.

Can I use map on a list in Python? ›

The map() function (which is a built-in function in Python) is used to apply a function to each item in an iterable (like a Python list or dictionary). It returns a new iterable (a map object) that you can use in other parts of your code.

How do you plot geographic data? ›

If you have data that is associated with specific geographic locations, use a geographic axes or chart to visualize your data on a map and provide visual context. The geographic axes and charts plot data over a map. You can pan and zoom in and out on the map.

How do I plot a KML file in Python? ›

Python Custom KML Points, Lines, Polygons, Circles - YouTube

How do you plot data on a map? ›

Plotting data on maps in R using ggmap - YouTube

Which library can be used to plot geographical data? ›

Plotly. Plotly is an open-source library which allows us to create interactive plots that can be used in dashboards or websites.

How can I create a map? ›

Start by heading to maps.google.com. Click on the menu icon on the top left hand side of the screen and select “Your Places.” (The menu icon is just to the left of the search bar on the top left hand side of your screen.) Select the maps tab. Navigate to the very bottom of that window and select “Create a Map.”

What are the different types of spatial data? ›

Spatial data are of two types according to the storing technique, namely, raster data and vector data.

What's the difference between KMZ and KML? ›

KML can include both raster and vector data, and the file includes symbolization. KML files are like HTML, and only contains links to icons and raster layers. A KMZ file combines the images with the KML into a single zipped file.

How do I validate a KML file? ›

KML is an XML file so first you can test if it's a well-formed XML file, which is a prerequisite to it being a valid KML file. Simply rename the KML file adding an . xml file extension then drag the file onto a web browser (Firefox, Chrome, etc.) to validate it.

What is KML file extension? ›

KML is a file format used to display geographic data in an Earth browser such as Google Earth. You can create KML files to pinpoint locations, add image overlays, and expose rich data in new ways.

How do you convert XY coordinates to latitude and longitude? ›

Calculate latitude and longitude using the formula: latitude = asin (z/R) and longitude = atan2 (y,x). In this formula, we have the values of x, y, z and R from step 2. Asin is arc sin, which is a mathematical function, and atan2 is a variation of the arc tangent function. The symbol * stands for multiplication.

How do map coordinates work? ›

The geographic coordinate system consists of latitude and longitude lines. Each line of longitude runs north–south and measures the number of degrees east or west of the prime meridian. Values range from -180 to +180°. Lines of latitude run east–west and measure the number of degrees north or south of the equator.

What is a map chart used for? ›

A map chart provides a visualization of a geographic region, which contains data from the underlying source data. It also provides the ability to highlight subdivisions of that geographic region through conditional formatting.

What is data mapping in Excel? ›

What is Excel data mapping? The meaning depends on the context. When transferring information into Microsoft Excel from another source, it means matching the data fields from the source with columns in your destination file.

How do I create a map of data in Excel? ›

Now it's time to create a map chart, so select any cell within the data range, then go to the Insert tab > Charts > Maps > Filled Map. If the preview looks good, then press OK. Depending on your data, Excel will insert either a value or category map.

Videos

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4. Map it with Python! Intro to GIS and Python mapping modules.
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5. Walkthrough: Mapping GIS Data in Python | Nicole Janeway Bills
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6. Introduction to Visualizing Geospatial Data with Python GeoPandas
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