I would like to select polygons that contain at least a point. I can use QGIS's tool called "Select by location: Select all buildings by location of point".
Is there a python alternative?
So far, I wrote a jupyter notebook and worked with GeoPandas.
I have tried
import geopandas as gpd
import pandas as pd
polygon_layer = gpd.read_file(r'file.shp')
excel = pd.read_excel('file2.xlsx')
points_layer = gpd.GeoDataFrame(excel, geometry=gpd.points_from_xy(excel.X, excel.Y))
subset = gpd.sjoin(polygon_layer, points_layer, how='inner', predicate='within')
However, the spatial join does not work as it returns an empty geoDataFrame
Related
I have a dataset which have an xml column and i am trying to export individual xmls as files with filename being in another column using codeworkbook
I filtered the rows i want using below code
def prepare_input(xml_with_debug):
from pyspark.sql import functions as F
filter_column = "key"
filter_value = "test_key"
df_filtered = xml_with_debug.filter(filter_value == F.col(filter_column))
approx_number_of_rows = 1
sample_percent = float(approx_number_of_rows) / df_filtered.count()
df_sampled = df_filtered.sample(False, sample_percent, seed=0)
important_columns = ["key", "xml"]
return df_sampled.select([F.col(c).cast(F.StringType()).alias(c) for c in important_columns])
It works till here. Now for the last part i tried this in a python task, but was complaining about the parameters (i should have set it up wrongly). But even if it works it will be as a single file i think.
from transforms.api import transform, Input, Output
#transform(
output=Output("/path/to/python_csv"),
my_input=Input("/path/to/input")
)
def my_compute_function(output, my_input):
output.write_dataframe(my_input.dataframe().coalesce(1), output_format="csv", options={"header": "true"})
I am trying to set it up in GUI like below
My question i guess is, what will be the code in the last Python task (write_file) after the prepare input so that i extract individual xmls (And if possible zip them into single file for download)
You can access the output dataset filesystem and write files into it in whatever format you want.
The documentation for that can be found here: https://www.palantir.com/docs/foundry/code-workbook/transforms-unstructured/#writing-files
(If you want to do it from a code repository it's very similar https://www.palantir.com/docs/foundry/transforms-python/unstructured-files/#writing-files)
By doing that you can create multiple different files or you can create a single zip file and write it into a dataset.
I have a folder where I will upload one file every month. The file will have the same format in every month.
First problem
The idea is to concatenate all the files in this folder into one file. Currently I am hardcoding the filenames (filename[0], filename[1], filename[2]..) but imagine later I will have 50 files, should I explicitly add them to the transform_df decorator? Is there any other method to handle this?
Second problem:
Currently I have let's say 4 files (2021_07, 2021_08, 2021_09, 2021_10) and I want whenever I add the file presenting 2021_12 data to avoid changing the code.
If I add input_5 = Input(path_to_2021_12_do_not_exists) the code will not be run and give an error.
How can I implement the code for future files and let the code ignore the input if it does not exist without manually each month add a new value to my code?
Thank you
# from pyspark.sql import functions as F
from transforms.api import transform_df, Input, Output
from pyspark.sql.functions import to_date, year, col
from pyspark.sql.types import StringType
from myproject.datasets import utils
from pyspark.sql import DataFrame
from functools import reduce
input_dir = '/Company/Project_name/'
prefix_filename = 'DataInput1_'
suffixes = ['2021_07', '2021_08', '2021_09', '2021_10', '2021_11', '2021_12']
filenames = [input_dir + prefix_filename + suffixe for suffixe in suffixes]
#transform_df(
Output("/Company/Project_name/Data/clean/File_concat"),
input_1=Input(filenames[0]),
input_2=Input(filenames[1]),
input_3=Input(filenames[2]),
input_4=Input(filenames[3]),
)
def compute(input_1, input_2, input_3, input_4):
input_dfs = [input_1, input_2, input_3, input_4]
dfs = []
def transformation_input(df):
# some transformation
return df
for input_df in input_dfs:
dfs.append(transformation_input(input_df))
dfs = reduce(DataFrame.unionByName, dfs)
return dfs
This question comes up a lot, the simple answer is that you don't. Defining datasets and executing a build on them are two different steps executed at different stages.
Whenever you commit your code and run the checks, your overall python code is executed during the renderSchrinkwrap stage, except for the compute part. This allows Foundry to discover what datasets exist and publish.
Publishing involves creating your dataset and putting whatever is inside your compute function is published into the jobspec of the dataset, so foundry knows what code to execute whenever you run a build.
Once you hit build on the dataset, Foundry will only pick up whatever is on the jobspec and execute it. Any other code has already run during your checks, and it has run just once.
So any dynamic input/output would require you to re-run checks on your repo, which means that some code change would have had to happen since the Checks is part of the CI process, not part of the build.
Taking a step back, assuming each of your input files has the same schema, Foundry would expect you to have all of those files in the same dataset as append transactions.
This might not be possible though, if for instance, the only indication of the "year" of the data is embedded in the filename, but your sample code would indicate that you expect all these datasets to have the same schema and easily union together.
You can do this manually through the Dataset Preview - just use the Upload File button or drag-and-drop the new file into the Preview window - or, if it's an "end user" workflow, with a File Upload Widget in a Workshop app. You may need to coordinate with your Foundry support team if this widget isn't available.
Bit late to the post although for anyone who is interested in an answer to most of the question. Dynamically determining file names from within a folder is not doable although having some level of dynamic input is possible as follows:
# from pyspark.sql import functions as F
from transforms.api import transform, Input, Output
from pyspark.sql.functions import to_date, year, col
from pyspark.sql.types import StringType
from myproject.datasets import utils
from pyspark.sql import DataFrame
# from functools import reduce
from transforms.verbs.dataframes import union_many # use this instead of reduce
input_dir = '/Company/Project_name/'
prefix_filename = 'DataInput1_'
suffixes = ['2021_07', '2021_08', '2021_09', '2021_10', '2021_11', '2021_12']
filenames = [input_dir + prefix_filename + suffixe for suffixe in suffixes]
inputs = {('input{}'.format(index)): Input(filename) for (index, filename) in enumerate(filenames))}
#transform(
output=Output("/Company/Project_name/Data/clean/File_concat"),
**inputs
)
def compute(output, **kwargs):
# Extract dataframes from input datasets
input_dfs = [dataset_df.dataframe() for dataset_name, dataset_df in kwargs.items()]
dfs = []
def transformation_input(df):
# some transformation
return df
for input_df in input_dfs:
dfs.append(transformation_input(input_df))
# dfs = reduce(DataFrame.unionByName, dfs)
unioned_dfs = union_many(*dfs)
return unioned_dfs
Couple points:
Created dynamic input dict.
That dict is read into the transform using **kwargs.
Using transform decorator not transform_df, we can extract the dataframes.
(not in question) Combine multiple dataframes using union_many function from transforms_verbs library.
I understand that meteor_score from nltk.translate.meteor_score calculates the METEOR-score for one hypothesis sentence based on a list of candidates.
But is there an implementation for calculating the score over an entire corpus as well or a way to do it, similar to the corpus_bleu implementation?
I couldn't find something for this case.
I have created something like this for my project:
#>>> nltk.download()
# Download window opens, fetch wordnet
#>>> from nltk.corpus import wordnet as wn
from nltk.translate.meteor_score import meteor_score
import numpy as np
def corpus_meteor(expected, predicted):
meteor_score_sentences_list = list()
[meteor_score_sentences_list.append(meteor_score(expect, predict)) for expect, predict in zip(expected, predicted)]
meteor_score_res = np.mean(meteor_score_sentences_list)
return meteor_score_res
Recently I started using dash for Data Visualization and I'm analyzing the Stock Data using qunadle API, but unable to get multiple dashboards of dropdown displaying the options of each dataset using a for loop like this
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import quandl
import plotly.graph_objs as go
import auth
api_key = auth.key
def easy_analysis(quandl_datasets):
try:
for dataset in quandl_datasets:
df = quandl.get(dataset,authtoken=api_key)
df = df.reset_index()
app = dash.Dash(__name__)
app.layout = html.Div([
html.H3(dataset),
dcc.Dropdown(
id=dataset,
options=[{'label' : s,'value' : s} for s in df.columns[1:]],
value=['Open'],
multi=True
),
dcc.Graph(id='dataset' + str(dataset))
])
#app.callback(
Output('dataset' + str(dataset),'figure'),
[Input(dataset,'value')]
)
def draw_graph(dataset):
graphs = []
for column in dataset:
graphs.append(go.Scatter(
x=list(df.Date),
y=list(df[column]),
name=str(column),
mode='lines'
))
return {'data' : graphs}
app.run_server(debug=True)
except Exception as e:
print(str(e))
easy_analysis(['NSE/KOTAKNIFTY','NSE/ZENSARTECH','NSE/BSLGOLDETF'])
The Output which I expected was having multiple dashboards with all the dropdown options one after the other. But the result what I got was having only one dashboard of the last item in the easy_analysis() function list
easy_analysis(['NSE/KOTAKNIFTY','NSE/ZENSARTECH','NSE/BSLGOLDETF']), considered only 'NSE/BSLGOLDETF'
what am I supposed do to fix this and get multiple dashboards of each dataset as provided in the list. I also checked the Dash User Guide, but could not get what I was looking for.
But, when passed only one argument for only one dataset with a for loop, the code works fine and the graph changes according to the option selected in the dropdown.
The code is here.
The code does not work because you are redefining a Dash app at each iteration of the for loop.
Even if you have three datasets, you need to define the Dash app and its layout only once.
You can make three requests to the Quandl API and - if possible - save everything in the same pandas Dataframe.
One question is whether you want to display all dropdowns and graphs (i.e. dropdown + graph for each Quandl dataset) or only one dropdown and one graph. I would suggest to start with the first approach, because it's much easier. Anyway, for the second approach you can have a look at this solution.
I am having trouble to access the coefficients of a support vector regression model (SVR) in scikit learn when the model is embedded in a pipeline and a grid search.
Consider the following example:
from sklearn.datasets import load_iris
import numpy as np
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVR
from sklearn.feature_selection import SelectKBest
from sklearn.pipeline import Pipeline
iris = load_iris()
X_train = iris.data
y_train = iris.target
clf = SVR(kernel='linear')
select = SelectKBest(k=2)
steps = [('feature_selection', select), ('svr', clf)]
pipeline = Pipeline(steps)
grid = GridSearchCV(pipeline, param_grid={"svr__C":[10,10,100],"svr__gamma": np.logspace(-2, 2)})
grid.fit(X_train, y_train)
This seems to work fine but when I try to access the coefficient of the best fitting model
grid.best_estimator_.coef_
I get an error message: AttributeError: 'Pipeline' object has no attribute 'coef_'.
I also tried to access the individual steps of the pipeline:
pipeline.named_steps['svr']
but could not find the coefficients there.
Just happened to come across the same problem and this post
had the answer:
grid.best_estimator_ contains an instance of the pipeline, which consists of steps. The last step should always be the estimator, so you should always find the coefficients at:
grid.best_estimator_.steps[-1][1].coef_