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Using python 3.7 , pandas 1.1.3 , Anaconda Jupyter Notebook
I am new into python and I have a following dataframe.
DF_1
Name Date
AAA 2000-09-01
BBB 2001-08-01
CCC 2002-07-01
AAA 2005-05-01
I just want to extract distinct values from 'Name' column and create a new dataframe (df_2) and insert into it with same column name.
Output df_2 should look like this
Name
AAA
BBB
CCC
298
You can get unique values in column (multiple columns) from pandas DataFrame using unique() or Series. unique() functions. unique() from Series is used to get unique values from a single column and the other one is used to get from multiple columns.
Using Python's import numpy, the unique elements in the array are also obtained. In the first step convert the list to x=numpy. array(list) and then use numpy. unique(x) function to get the unique values from the list.
And you can use the following syntax to select unique rows across specific columns in a pandas DataFrame: df = df. drop_duplicates(subset=['col1', 'col2', ...])
Alternatively, You can also use DataFrame.filter () method to create a copy and create a new DataFrame by selecting specific columns. Yields output same as above. 3. Using DataFrame.transpose () Method DataFrame.transpose () method is used to transpose index and column. It reflects the DataFrame writing rows as columns and vice-versa.
So to replace values from another DataFrame when different indices we can use: Now the values are correctly set: You can use Pandas merge function in order to get values and columns from another DataFrame. For this purpose you will need to have reference column between both DataFrames or use the index.
The original dataframe looks like this import pandas as pd df = pd.DataFrame(data = temp['data']['weather']) df.head() The first one is simple, it takes a dataframe and the name of a column, and it will extract the column into a new dataframe.
You can create new pandas DataFrame by selecting specific columns by using DataFrame.copy (), DataFrame.filter (), DataFrame.transpose (), DataFrame.assign () functions. DataFrame.iloc [] and DataFrame.loc [] are also used to select columns.
Try this:
df_2 = DF_1[['Name']].drop_duplicates()
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