I want to add two df columns df['Date'] and df['hour'] to create the column timestamp

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The problem is the hour column and the date column are like this:

Is there any way to add them to get a column starting with 2019-07-01 7:00:00 and so on

You can do:

df['datetime'] = pd.to_datetime(df['Date']) + pd.to_timedelta('1H') * df['Hour']

# or
# df['datetime'] = pd.to_datetime(df['Date']) + pd.to_timedelta(df['Hour'], unit='H')

How to sum two columns in a pandas DataFrame in Python, Select each column of DataFrame df through the syntax df["column_name"] and add them together to get a pandas Series containing the sum of each row. Create a new column in the DataFrame through the syntax df["new_column"] and set it equal to this Series to add it to the DataFrame. Case 2: Add Multiple Columns to Pandas DataFrame. What if you want to add multiple columns to your DataFrame? If that’s the case, simply separate those columns using a comma. For example, let’s say that you want to add two columns to your DataFrame: The ‘Price’ column; and; The ‘Discount’ column

df['datetime'] = df[['Date', 'hour']].apply(lambda x: ' '.join(x), axis=1)


df['datetime']= pd.to_datetime(df['datetime'])

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You can try something like this

df.apply(lambda t : pd.datetime.combine(t['date_column_name'],t['time_column_name']),1)

If both columns are string you can simply concatenate it as well

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Merge, join, and concatenate — pandas 0.20.3 documentation, Suppose we wanted to associate specific keys with each of the pieces of the chopped up DataFrame. We can When gluing together multiple DataFrames ( or Panels or. indicator : Add a column to the output DataFrame called _merge with� add column from raw df to groped df in pyspark-1. and my spark_df dataframe has three columns: is traditionally one of the two main divisions of narratives

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