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Tag: dataframe

Why does pandas.DataFrame.merge return dataframes with different column types than the input dataframes?

Slightly expanding the Example 1: Merge on Multiple Columns with Different Names, results in the following Python code using Pandas pandas.DataFrame.merge: The resulting output (I’ve added line numbers): Notice the type of a2 and d columns in the resulting df_merge dataframe on lines 24 through 27 have changed from the original int64 to float64. Why would it need to change

Appending data with unequal data frame dimensions

What is the best way to append data using matching column names from two different data frames with differing dimensions? Scenario: Df1 = 350(rows)x2778(columns) Df2 = 321×2910 Df1 has <2778 columns with the exact same name as <2910 columns in Df2. -It could be 500 columns in each data frame as an example that have equivalent names What I want

Pandas: Merge Dataframes Based on Condition but Keep NaN

I have two dataframes, df1 and df2, which I would like to merge on the column ‘id’ where the ‘triggerdate’ from df1 falls between the ‘startdate’ and ‘enddate’ of df2, however, keep the rows where there’s no match. df1: df2: Expected Output: The approach that I have taken so far is: However, this approach does the following 1) Matches the

How to iterate the loop if the condition is not met

I am trying to get the id of respective movie name in that i need to check whether the url is working or not . If not then i need to append the movie name in the empty list print(movie_buff_uuid) if i passed the data2 in the above loop i am getting this error urllib.error.HTTPError: HTTP Error 404: Not Found

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