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Import pandas as pd

Import Pandas. If you want to use the Pandas module in your working path. Just import it with the following command. It is like calling your servant to work for you. import pandas as pd What does import pandas as pd mean? Import = Bring this functionality or library to my python script Pandas = The library you want to import, in this case, it's pandas As = The python nomenclature for creating as alias import pandas as pd. In this statement, we're importing the Pandas library with an alias, or variable name of pd. We could just as simply right import pandas, however, each time we'd write pandas.function () to access some part of the Pandas library, which contains many functions

Import Pandas as pd

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  2. import pandas as pd d = {'one' : pd.series([1., 2., 3.], index = ['a', 'b', 'c']), 'two' : pd.series([1., 2., 3., 4.], index = ['a', 'b', 'c', 'd'])} df = pd.Dataframe(d
  3. import pandas as pd d = {'one' : pd.Series( [1, 2, 3], index= ['a', 'b', 'c']), 'two' : pd.Series( [1, 2, 3, 4], index= ['a', 'b', 'c', 'd'])} df = pd.DataFrame(d) print df.loc['b'] Its output is as follows −. one 2.0 two 2.0 Name: b, dtype: float64. The result is a series with labels as column names of the DataFrame
  4. import pandas as pd. This is supposed to import the Pandas library into your (virtual) environment. However, it only throws the following import error: no module named pandas! >>> import pandas as pd ImportError: No module named pandas on line 1 in main.py. You can reproduce this error in the following interactive Python shell
  5. Nachdem du die Datei heruntergeladen hast, kannst du Python starten und Pandas wie folgt importieren. import pandas as pd. Numpy bildet zwar die Basis für Pandas, muss aber nicht direkt in die Programmierumgebung importiert werden. Die Funktion, um die sich hier alles dreht, heißt .read_csv(). Diese werden wir im folgenden auseinandernehmen

import pandas as pd - Bring Pandas to Python - Data

To get the requests object in text format: import requests import pandas as pd url = r'http://test.url' r = requests.get (url) r.text #this will return the data as text in csv format import pandas as pd import matplotlib.pyplot as plt import numpy as np df = pd.DataFrame(np.random.randn(100, 5), columns=list('ABCDE')) df=df.cumsum() # Return cumulative sum over a DataFrame or Series axis df.plot() plt.show( import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('data.csv') df.plot() plt.show( import pandas as pd df = pd.read_csv (path_to_file) Here, path_to_file is the path to the CSV file you want to load. It can be any valid string path or a URL (see the examples below). It returns a pandas dataframe import pandas as pd df = pd.read_csv (r'Path where the CSV file is stored\File name.csv') print (df) Next, you'll see an example with the steps needed to import your file. Importing the Data into Pytho

So here is the complete code to convert the array to a DataFrame with an index: import numpy as np import pandas as pd my_array = np.array ([ [11,22,33], [44,55,66]]) df = pd.DataFrame (my_array, columns = ['Column_A','Column_B','Column_C'], index = ['Item_1', 'Item_2']) print (df) print (type (df) View import pandas as pd.docx from COMPUTER S 123 at Andhra University. import pandas as pd import numpy as np heights_A = pd.Series([176.2, 158.4, 167.6, 156.2, 161.4]) heights_A.index = ['s1', Study Resource The basic process of loading data from a CSV file into a Pandas DataFrame (with all going well) is achieved using the read_csv function in Pandas: # Load the Pandas libraries with alias 'pd' import pandas as pd # Read data from file 'filename.csv import pandas as pdimport numpy as npimport matplotlib.pyplot as plt创建对象¶pandas中两种类型:series和dataframe.下面创建两种基本类型。 通过传递一个list来创建Series, pandas 会默认创建整型索引s = pd .Series([1,3.. Pandas To CSV Pandas .to_csv() Parameters. At a bare minimum you should provide the name of the file you want to create. After that I recommend setting Index=false to clean up your data.. path_or_buf = The name of the new file that you want to create with your data. If you don't specify a path, then Pandas will return a string to you

Pandas series is a one-dimensional data structure. It can hold data of many types including objects, floats, strings and integers. You can create a series by calling pandas.Series (). An list, numpy array, dict can be turned into a pandas series. You should use the simplest data structure that meets your needs In this article, you will learn how to convert pandas DataFrame into a Python dictionary.It explains creating different kinds of dictionaries from pandas DataFrame. Data Analyst needs to collect the data from heterogeneous sources like CSV files or SQL tables or Python data structures like a dictionary, list, etc import pandas as pd S = pd. Series ([11, 28, 72, 3, 5, 8]) print (S) 0 11 1 28 2 72 3 3 4 5 5 8 dtype: int64 Wir haben in unserem Beispiel keinen Index definiert. Trotzdem sehen wir zwei Spalten in der Ausgabe: Die rechte Spalte zeigt unsere Daten, die linke Spalte stellt den Index dar. Pandas erstellt einen Default-Index, der bei 0 beginnt und bis 5 läuft. Wir können direkt auf die Indizes. import pandas as pd data = pd.read_csv ('filename.csv') df = pd.DataFrame(data, columns= ['Region','Country', 'Total Profit']) df. You can apply the head() function as well in order to print the first 5 rows of your dataset from the csv file. import pandas as pd data = pd.read_csv ('filename.csv') data.head() References: pandas.read_csv; Previous. Next. Pandas Tutorials — Pandas Introduction. import numpy as np import pandas as pd s = pd.Series(['python', 3, np.nan, 12, 6, 8]) print(s) Run. Output. 0 python 1 3 2 NaN 3 12 4 6 5 8 dtype: object. As the elements belong to different datatypes, like integer and string, the datatype of all the elements in this pandas series is considered as object. But when you access the elements individually, the corresponding datatype is returned.

Example 1: Insert New Column as First Column. The following code shows how to insert a new column as the first column of an existing DataFrame: import pandas as pd #create DataFrame df = pd.DataFrame ( {'points': [25, 12, 15, 14, 19], 'assists': [5, 7, 7, 9, 12], 'rebounds': [11, 8, 10, 6, 6]}) #view DataFrame df points assists rebounds 0 25 5. import pandas as pd from datetime import datetime, timedelta as delta ndays = 10 start = datetime (2018, 6, 1) dates = [start-delta (days = x) for x in range (0, ndays)] start2 = datetime (2018, 5, 28) dates2 = [start2-delta (days = x) for x in range (0, ndays)] values = [25, 50, 15, 67, 70, 9, 28, 30, 32, 12] values2 = [32, 54, 18, 61, 72, 19, 21, 33, 29, 17] ts = pd. Series (values, index. from pathlib import Path import pandas as pd # This is the only line you need to register `.path` as an accessor # on any Series or Index in pandas. from pandas_path import path # we'll make an example series from the py files in this repo; # note that every element here is just a string--no need to make Path objects yourself file_paths = pd. Series (str (s) for s in Path (). glob ('**/*.py. I have pandas installed in my default python site-packages folder. When importing pandas in Pycharm, everything goes smoothly: in: import pandas as pd out: `C:\Users\sx449_000\AppData\Local\Programs\Python\Python36-32\python.exe C:/Users..

How to Install & Import Pandas in Python - Data Course

python 3.x - import pandas as pd ImportError: No module ..

Pastebin.com is the number one paste tool since 2002. Pastebin is a website where you can store text online for a set period of time read_table() Methode zum Laden einer Textdatei in Pandas dataframe. read_table() ist ein anderer Ansatz, um Daten aus einer Textdatei in Pandas Dataframe zu laden. Beispiel.txt: 45 apple orange banana mango 12 orange kiwi onion tomato Der Code: # python 3.x import pandas as pd df = pd.read_table( 'sample.txt',header=None,sep= ) print(df) Ausgabe import pandas as pd import PyPDF2. Then we will open the PDF as an object and read it into PyPDF2. pdfFileObj = open('2017_SREH_School_List.pdf', 'rb') pdfReader = PyPDF2.PdfFileReader(pdfFileObj) Now we can take a look at the first page of the PDF, by creating an object and then extracting the text (note that the PDF pages are zero-indexed). We can see that its really messy and comes in the.

So, Pandas provides us the functions to convert datasets in other formats to the Data frame. An excel file has a '.xlsx' format. Before we get started, we need to install a few libraries. pip install pandas pip install xlrd For importing an Excel file into Python using Pandas we have to use pandas.read_excel() function >>> import pandas as pd Traceback (most recent call last): File yourApp.py, line 1, in <module> import pandas as pd ImportError: No module named pandas Pandas is already installed in many environments such as in Anaconda

Python Pandas.txt - import pandas as pd import numpy as np heights_A = pd.Series[176.2 158.4 167.6 156.2 161.4 index ='s1's2's3's4's5. Python Pandas.txt - import pandas as pd import numpy as np... School Andhra University; Course Title STATISTICS 786; Uploaded By shankarkasarapu. Pages 7 Ratings 83% (6) 5 out of 6 people found this document helpful; This preview shows page 1 - 3 out of 7 pages. Import CSV Files As Pandas DataFrame With skiprows, skipfooter, usecols, index_col and header Options. rashida048; April 20, 2020; Data Science; 0 Comments ; CSV files are a very common and popular format of storing data. Data Scientists deal with csv files almost regularly. Pandas not only has the option to import a dataset as a regular Pandas DataFrame, also there are other options to clean. import pandas as pd. It has successfully imported the pandas library to our project. The next step is to use the read_csv function to read the csv file and display the content. Step 2: Use read_csv function to display a content. Pandas read_csv function has the following syntax. pandas.read_csv('filename or filepath', ['dozens of optional parameters']) The read_csv method has only one required. import pandas as pd Simply imports the library the current namespace, but rather than using the name pandas, it's instructed to use the name pd instead. This is just so you can do pd.whatever instead of having to type out pandas.whatever all the time if you just do import pandas Same with import numpy as np. You could give it any name you want . import pandas as giant_panda_bear giant_panda.

Given : import pandas as pd d = one : pd

Project description. pandas-charm is a small Python package for getting character matrices (alignments) into and out of pandas . Use this library to make pandas interoperable with BioPython and DendroPy. Convert between the following objects: BioPython MultipleSeqAlignment <-> pandas DataFrame. DendroPy CharacterMatrix <-> pandas DataFrame import pandas as pd import numpy as np import sklearn from sklearn import datasets, linear_model from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.metrics import mean_squared_error, r2_score, accuracy_score from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import Pipeline from sklearn import preprocessing.

Using Pandas to CSV () with Perfection. Pandas to_csv method is used to convert objects into CSV files. Comma-separated values or CSV files are plain text files that contain data separated by a comma. This type of file is used to store and exchange data. Now let us learn how to export objects like Pandas Data-Frame and Series into a CSV file import pandas as pd from pyspark.sql.functions import col, pandas_udf from pyspark.sql.types import LongType # Declare the function and create the UDF def multiply_func(a: pd.Series, b: pd.Series) -> pd.Series: return a * b multiply = pandas_udf(multiply_func, returnType=LongType()) # The function for a pandas_udf should be able to execute with local pandas data x = pd.Series([1, 2, 3]) print.

Python Pandas - DataFrame - Tutorialspoin

How to Fix ImportError: No module named pandas [Mac

You have some data in a relational database, and you want to process it with Pandas. So you use Pandas' handy read_sql() API to get a DataFrame—and promptly run out of memory. The problem: you're loading all the data into memory at once. If you have enough rows in the SQL query's results, it simply won't fit in RAM. Pandas does have a batching option for read_sql(), which can reduce. Python之Pandas使用教程 1.Pandas概述 Pandas是Python的一个数据分析包,该工具为解决数据分析任务而创建。 Pandas纳入大量库和标准数据模型,提供高效的操作数据集所需 pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Install pandas now! Getting started. Install pandas. Getting started. Documentation. User guide Here make a dataframe with 3 columns and 3 rows. The array np.arange(1,4) is copied into each row. import pandas as pd import numpy as np df = pd.DataFrame([np.arange(1,4)],index=['a','b','c'], columns=[X,Y,Z]) Results: Now reindex this array adding an index d. Since d has no value it is filled with NaN. df.reindex(index=['a','b','c','d'] Import small and large data using pandas (CSV, Excel, Tab, JSON, SQL, and Parquet files) Renesh Bedre 5 minute read. Importing datasets is a key step in data analysis and visualization tasks. The source data can be saved in different file formats such as CSV (comma-separated value), tab-separated text, Excel, SQL, or JSON files

How to quickly load a JSON file into pandas Feeding Your Inner Panda: Pandas and NumPy for Data Science (Part 1) Every Data Scientist knows that Pandas and NumPy are very powerful libraries, due its capabilities and flexibilities. In this article, we're going to advanced concepts discuss in detail and how to utilize the same during Data Science implementation import pandas as pd import numpy as np np.random.seed(1234) df = pd.DataFrame(np.random.randn(15,4), columns=['A1', 'A2', 'A3', 'A4']) boxplot = df.boxplot(column=['A1', 'A2', 'A3']) Output: In the above program, we first import pandas and numpy libraries as pd and np respectively. After importing these libraries, we create a dataframe of random seed array and then define then plot this random. (base) @ ~ % import pandas as pd zsh: command not found: import (base) @~ % python main.py python: can't open file 'main.py': [Errno 2] No such file or directory キャンセル. 完了する. 2020/12/08 21:34 . このコメントからわかるのは、あなたが「Pythonのプログラムをどうやって実行するのか」わかっていないということです。.

CSV in Python importieren mit Pandas - StatisQu

  1. problème import pandas as pd × Après avoir cliqué sur Répondre vous serez invité à vous connecter pour que votre message soit publié. × Attention, ce sujet est très ancien
  2. _rate = rate_data.
  3. Computer Science. Computer Science questions and answers. 1 # -*- coding: utf-8 -*- 2 import pandas as pd 3 from sklearn.model_selection import cross_val_score 4 from sklearn.preprocessing import StandardScaler 5 from sklearn import feature_selection 6 from sklearn.linear_model import LogisticRegression 8 train = pd.read_csv ('./data/train.csv.

Loading a CSV into pandas. Unnamed: 0 first_name last_name age preTestScore postTestScore; 0: False: False: Fals #!/usr/bin/env python3 import pandas as pd import numpy as np data = {'coins' : 22, 'pens' : 3, 'books' : 28} s = pd.Series(data) print(s) The example creates a series object from a dicionary of items. $ python series_dict.py coins 22 pens 3 books 28 dtype: int64 The index consits of the names of the items. Pandas series retriev

Python Pandas - Series - Tutorialspoin

In this article, you will learn how to import and manipulate large datasets in Python using pandas. How to Import Pandas. To use pandas in a Python script, you will first need to import it. It is convention to import pandas under the alias pd, like this: import pandas as pd. If pandas is not already installed on your machine, you will encounter. To import Pandas and NumPy in your Python script, add the below piece of code: import pandas as pd import numpy as np As Pandas is dependent on the NumPy library, we need to import this dependency import pandas as pd s = pd.Series([10,15,18,22]) df=pd.DataFrame(s) df.columns=['List1'] To Rename the default column of Data Frame as List1 df['List2']=20 To create a new column List2 with all values as 20 df['List3']=df['List1']+df['List2'] Add Column1 and Column2 and store in New column List3 print(df) Output- List1 List2 List3 0 10 20 30 1 15 20 35 2 18 20 38 3 22 20 42. # importing pandas module import pandas as pd # importing csv from link data = pd.read_csv(nba.csv) # making copy of team column new = data[Team].copy() # concatenating team with name column # overwriting name column data[Name]= data[Name].str.cat(new, sep =, ) # display data Output: As shown in the output image, every string in the Team column having same index as string in Name. If you want to check that the DataFrame only contains columns in the schema, specify strict=True: import pandas as pd import pandera as pa from pandera import Column, DataFrameSchema schema = DataFrameSchema( {column1: Column(pa.Int)}, strict=True) df = pd.DataFrame( {column2: [1, 2, 3]}) schema.validate(df) Traceback (most recent call las

Pandas Getting Started - W3School

First we will start with the imports: #Use to import pandas import pandas as pd #Use to import the file into google Colab drive from google.colab import files #Use to import io, which opens the. import pandas as pd pd.read_csv('table.csv', sep=';', index_col=0) output: name occupation index 1 Alice Salesman 2 Bob Engineer 3 Charlie Janitor Table without row names or index and commas as separators. file: table.csv. Alice,Saleswoman Bob,Engineer Charlie,Janitor code: import pandas as pd pd.read_csv('table.csv', names=['name','occupation']) output: name occupation 0 Alice Salesman 1 Bob. Example import string import numpy as np import pandas as pd generate sample DF with various dtypes df = pd.DataFrame({ 'int32': np.random.randint(0, 10**6, 10), 'int64': np.random.randint(10**7, 10**9, 10).astype(np.int64)*10, 'float': np.random.rand(10), 'string': np.random.choice([c*10 for c in string.ascii_uppercase], 10), }) In [71]: df Out[71]: float int32 int64 string 0 0.649978 848354. Type import pandas as pd in python (IDLE) shell. 7. If it executed without error(it means pandas is installed on your system) Data Handling using Pandas -1 . Visit : python.mykvs.in for regular updates Data Structures in Pandas Two important data structures of pandas are-Series,DataFrame 1. Series Series is like a one-dimensional array like structure with homogeneous data. For example, the. import pandas as pd import numpy as np. It is considered good practice to import pandas as pd and the numpy scientific library as np. This action allows you to use pd or np when typing commands. Otherwise, it would be necessary to enter the full module name every time. It is vital to import the Pandas library each time you start a new Python environment. Series and DataFrames. Python Pandas.

import pandas as pd #access the excel sheet using python df = pd.read_excel(io ='countries.xlsx', index_col=TableName) print(df) Output CountryCode ShortName LongName TableName Afghanistan AFG Afghanistan Islamic State of Afghanistan Albania ALB Albania Republic of Albania Algeria DZA Algeria People's Democratic Republic of Algeria American Samoa ASM American Samoa American Samoa Andorra ADO. Example: Plot percentage count of records by state. import matplotlib.pyplot as plt import matplotlib.ticker as mtick # create dummy variable then group by that # set the legend to false because we'll fix it later df.assign(dummy = 1).groupby( ['dummy','state'] ).size().groupby(level=0).apply( lambda x: 100 * x / x.sum() ).to_frame().unstack.

10 minutes to pandas — pandas 1

Explanation: In this code, firstly, we have imported the pandas and numpy library with the pd and np alias. Then, we have taken a variable named info that consist of an array of some values. We have called the info variable through a Series method and defined it in an a variable.The Series has printed by calling the print(a) method.. Python Pandas DataFram Pandas has a function read csv files, .read_csv (filename). This loads the csv file into a Pandas data frame. df = pd.read_csv ('nations.csv') Pandas works with dataframes which hold all data. Data frames are really cool data structures, they let you grab an entire row at once, by using it's header name Import Tabular Data from CSV Files into Pandas Dataframes. Using the read_csv() function from the pandas package, you can import tabular data from CSV files into pandas dataframe by specifying a parameter value for the file name (e.g. pd.read_csv(filename.csv)).Remember that you gave pandas an alias (pd), so you will use pd to call pandas functions 导入 pandas:import pandas as pd numpy+pandas除了效率对比excel还有什么功能上的优势吗 - —— python优势:1. 处理超大量数据(excel 2010 最多100w行)2 3. Pandas DataFrames (with column names) make it very easy to keep track of data. 4. Pandas is used when data is in Tabular Format, whereas Numpy is used for numeric array based data manipulation. 2.1.1. Installing Pandas Installing Pandas is very similar to installing NumPy. To install Pandas from command line, we need to type in: pip install.

Python Data Analysis with Pandas and Matplotli

  1. import pandas as pd from pyspark.sql.functions import col, pandas_udf from pyspark.sql.types import LongType # Declare the function and create the UDF def multiply_func (a: pd. Series, b: pd. Series)-> pd. Series: return a * b multiply = pandas_udf (multiply_func, returnType = LongType ()) # The function for a pandas_udf should be able to execute with local Pandas data x = pd. Series ([1, 2, 3.
  2. By default Pandas_Alive will create a tqdm progress bar when saving to a file, for the number of frames to animate, and update the progres bar after each frame. import pandas_alive covid_df = pandas_alive.load_dataset() # add a filename=movie.mp4 or movie.gif to save to, in order to see the progress bar in action covid_df.plot_animated(enable.
  3. Read SQL query from psycopg2 into pandas dataframe - connect_psycopg2_to_pandas.py. Skip to content. All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. jakebrinkmann / connect_psycopg2_to_pandas.py. Created Jul 3, 2017. Star 55 Fork 6 Star Code Revisions 1 Stars 55 Forks 6. Embed. What would you like to do? Embed Embed this gist i

Intro to data structures — pandas 1

Dynamic Programming for Portfolio Optimization . Sample dataframe : import pandas as pd. df = pd.DataFrame({'InvestmentName':['New York','Chicago','Dallas. import pandas as pdimport osimport requests. #Read in data using pandas. train_df = pd.read_csv ('hourly_rate.csv') #Check data has been read in properly. print (train_df.head ()) #Split the data into inputs and targets. #Create a dataframe with all training data except the target column 3. Import Multiple Excel Sheet into Pandas DataFrame. Multiple Excel Sheets can be read into Pandas DataFrame by passing list in the sheet_name parameter e.g. [0, Salary Info] will load the first sheet and sheet named Salary Info as a dictionary of DataFrame.. import pandas as pd # Read multiple excel file sheets as dictionary of DataFrame df = pd.read_excel(r'D:\Python\Tutorial. import pandas as pd from pathlib import Path src_file = Path. cwd / 'shipping_tables.xlsx' df = pd. read_excel (src_file, header = 1, usecols = 'B:F') The resulting DataFrame only contains the data we need. In this example, we purposely exclude the notes column and date field: The logic is relatively straightforward. usecols can accept Excel ranges such as B:F and read in only those columns.

nameerror name pd is not defined Error : Remove it Easil

Estou iniciando na área de machine learning, seguindo um site da internet que sugeriu o modelo inicial a seguir: import pandas as pd from sklearn import linear_model import matplotlib.pyplot as plt dataframe = pd.read_fwf('brain_body.txt') x_values = dataframe[['Brain']] y_values = dataframe[['Body']] body_reg = linear_model.LinearRegression() body_reg.fit(x_values,y_values) plt.scatter(x. There's no out-of-the-box way to do this so one answer is to sort the dataframe so that the correct values for each duplicate are at the end and then use drop_duplicates (keep='last') Example: drop duplicated rows, keeping the values that are more recent according to column year: import pandas as pd df = pd.DataFrame( { 'title': ['bar','bar. Posted: 2019-09-20 / Tags: Python, pandas. Tweet. There are following ways to check the version of pandas used in the script. Get version number: __version__ attribute. Print detailed information such as dependent packages: pd.show_versions () See the following post for how to check the installed pandas version with pip command. Sponsored Link Python Pandas Series. The Pandas Series can be defined as a one-dimensional array that is capable of storing various data types. We can easily convert the list, tuple, and dictionary into series using series ' method. The row labels of series are called the index. A Series cannot contain multiple columns import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion.

How to Transpose Pandas DataFrame - Data to Fis

Pandas: Split a given dataframe into groups and display target column as a list of unique values Last update on August 15 2020 09:51:25 (UTC/GMT +8 hours) Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-20 with Solution. Write a Pandas program to split a given dataframe into groups and display target column as a list of unique values. Test Data: id type book 0 A 1 Math 1 A 1 Math. First,import the pandas. Next, take a dictionary and convert into dataframe and store in df. Then, write the command df.Actor.str.split (expand=True). This means that the column 'Actor' is split into 2 columns on the basis of space and then print. #splitting on the basis of single space. Here, the you can see in the output that ' Actor.

Matplotlib Cyberpunk Style · MatplotblogHow to get Shape or Dimensions of Pandas DataFrame?[English MT] School Girl Courage Test 5 1Introduction to Pandas in Python - GeeksforGeeks
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