Tag : pandas

——Exploring the “Ontario Climate Change Data Portal”: A Case Study in Data Exploration Prompts searchable table Examples Download csv file from internet ChatGPT prompt I want you to act as a Python developer tasked with downloading a CSV file from the internet using the provided URL: https://lamps.math.yorku.ca/OntarioClimate/data/content/grids/Historical/Monthly_2m_temperature_8964_ERA5_January1981toMarch2021.csv. To accomplish this, write a Python script that ..

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There are a few Python packages that can be used to read RData files. Here are three popular ones: #install rpy2 !pip install rpy2 #An example of how to use rpy2 to load an RData file: #—————————————————- import rpy2.robjects as robjects # Load RData file robjects.r[‘load’](‘file.RData’) # Access R objects from Python r_var = robjects.globalenv[‘var_name’] ..

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paintings of pandas with AI Art generators

This post is a comprehensive guide on how to use AI art generators to paint a pandas or a group of pandases. It includes 20 text to image prompt examples for users to experiment with and improve their painting skills.The applicable popular AI art generators include NightCafe, DALL-E 2, Midjourney, Dream by WOMBO, craiyon, DeepDreamGenerator, ..

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a beautiful landscape

Group DataFrame using a mapper or by a Series of columns.Return a GroupBy object, grouped by values in column named “col”.Grouping and aggregation functions to help you to learn features of your dataset, like the sum, mean, or average value of a group of elements. The sample code below may help. Prepare data import pandas as ..

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A beautiful extreme wide aerial view of snowy mountains

It is easy to change the layout, sort, reindex, rename, and subset table data using pandas commands. The simple code below shows you how to do this easily. The functions include melt(),pivot,sort_values,rename,sort_index,drop,filter,query,iloc,loc,iat,at and drop_duplicates. Prepare data #dowonload https://github.com/ziwangdeng/Data/blob/main/Vancouver_weather2010to2019_v00.csv import pandas as pd df=pd.read_csv(‘Vancouver_weather2010to2019_v00.csv’) cols=df.columns df1=df[cols[:10]] df2=df[cols[10:]] ll=len(df) df3=df.head(5000) df4=df.tail(ll-5000) df.columns Index([‘Unnamed: 0’, ‘Longitude (x)’, ‘Latitude ..

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Beautiful happy picturesque charming lone maple trees

A comprehensive understanding of the overall situation of the data is the first step in data analysis. The following sample code shows you how to do this simply with pandas. Use of functions columns, keys(),axes,dtypes,info(),describe(),describe(include=object),isna().sum(),nunique(),value_counts(),len,shape,nsmallest(), nlargest(),sample(),head and tail. Read data #dowonload https://github.com/ziwangdeng/Data/blob/main/Vancouver_weather2010to2019_v00.csv import pandas as pd df=pd.read_csv(‘Vancouver_weather2010to2019_v00.csv’) Check column names df.columns Index([‘Unnamed: 0’, ‘Longitude (x)’, ..

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trees in spring, summer, autumn and winter

Example code for developing a regression model with keras. It can also answer following questions: Prepare data Read data import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split # create range of monthly dates download_dates = pd.date_range(start=’2019-01-01′, end=’2020-01-01′, freq=’MS’) # URL from Chrome DevTools Console base_url = (“https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&” ..

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A scenic autumn landscape

Example code for creating and adding new features to a data frame using the feature-engine. It also answer following questions: Math features Code import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from feature_engine.creation import MathFeatures from feature_engine.creation import RelativeFeatures from feature_engine.creation import CyclicalFeatures # create range of ..

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painting a breathtaking aerial view of lakes and mountains

Example code about how to extract several date and time features from datetime variables with feature-engine. It can answer following questions: import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from feature_engine import transformation as vt # create range of monthly dates download_dates = pd.date_range(start=’2019-01-01′, end=’2020-01-01′, freq=’MS’) # ..

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Example python code for handling missing data (ref:Python feature engineering cookbook ). Also answer the following questions: import pandas as pd from sklearn.model_selection import train_test_split from sklearn.impute import SimpleImputer from feature_engine.missing_data_imputers import MeanMedianImputer from feature_engine.imputation import ArbitraryNumberImputer from feature_engine.imputation import EndTailImputer from feature_engine.imputation import CategoricalImputer from feature_engine.imputation import RandomSampleImputer from feature_engine.imputation import AddMissingIndicator from feature_engine.imputation ..

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