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I have 2 columns in my data frame and I need to merge it into 1 single column
Index A Index B
0 A 0 NAN
1 NAN 1 D
2 B 2 NAN
3 NAN 3 E
4 C 4 NAN
there will always be one 1 value across the Columns, i want my result to look like
0 A
1 B
2 C
3 D
4 E
416
Step #1: Load numpy and Pandas. Step #2: Create random data and use them to create a pandas dataframe. Step #3: Convert multiple lists into a single data frame, by creating a dictionary for each list with a name. Step #4: Then use Pandas dataframe into dict.
To start, you may use this template to concatenate your column values (for strings only): df['New Column Name'] = df['1st Column Name'] + df['2nd Column Name'] + ... Notice that the plus symbol ('+') is used to perform the concatenation.
How to Merge Two Columns in Pandas ? : 3 Steps Only Step 1: Import the Necessary Packages Numpy and Pandas Packages are only required for this tutorial, therefore I am... Step 2: Create a Dataframe For the demonstration purpose, I am creating a Dataframe manually. You can apply the same... Step 3: ...
Let us see how to join two Pandas DataFrames using the merge () function. Returns : A DataFrame of the two merged objects. Example 2 : Merging two Dataframe with different number of elements : If we use how = "Outer", it returns all elements in df1 and df2 but if element column are null then its return NaN value.
You can use the following syntax to combine two text columns into one in a pandas DataFrame: df[' new_column '] = df[' column1 '] + df[' column2 '] If one of the columns isn’t already a string, you can convert it using the astype(str) command: df[' new_column '] = df[' column1 ']. astype (str) + df[' column2 ']
A named Series object is treated as a DataFrame with a single named column. The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored.
Option 1
df.stack().dropna().reset_index(drop=True)
0 A
1 D
2 B
3 E
4 C
dtype: object
Option 2 If Missing values are always alternating
df.A.combine_first(df.B)
Index
0 A
1 D
2 B
3 E
4 C
Name: A, dtype: object
Option 3 What you asked for
df.A.append(df.B).dropna().reset_index(drop=True)
0 A
1 B
2 C
3 D
4 E
dtype: object
Option 4 Similar to 3 but over arbitrary number of columns
pd.concat([i for _, i in df.iteritems()]).dropna().reset_index(drop=True)
0 A
1 B
2 C
3 D
4 E
dtype: object
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