Combine two datasets of equal length in Python

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I've got two datasets of equal length. Both only one column. I'm trying to combine them and make one dataset with two columns. What I've tried gives me one column with all the values from the first dataframe. But the second column is al NaN's. Please help.

I have tried .join & .merge & pd.concat & .add & ...

df_low_rename = df_low_sui.rename(index=str, columns={'suicides/100k pop': 'low_gdp'})
df_high_rename = df_high_sui.rename(index=str, columns={'suicides/100k pop': 'high_gdp'})

df_combined = df_low_rename.add(df_high_rename)


Pandas merge function works.

Dataset 1:

import pandas as pd
data = [['Alex',10],['Bob',12],['Clarke',13]]
df1 = pd.DataFrame(data,columns=['Name','Age'])


      Name  Age
 0    Alex   10
 1     Bob   12
 2  Clarke   13

Dataset 2:

data2 = [['Alex','Science'],['Bob','Physics'],['Clarke','Social']]
df2 = pd.DataFrame(data2,columns=['Name','Courses'])


      Name  Courses
 0    Alex  Science
 1     Bob  Physics
 2  Clarke   Social

merging the datasets:



        Name  Age  Courses
   0    Alex   10  Science
   1     Bob   12  Physics
   2  Clarke   13   Social

Pandas merge two datasets with same number of rows, With concat and merge I will get only first thousand combined and rest is filled with nan (I double checked that both are same size), and with� In a many-to-one join, one of your datasets will have many rows in the merge column that repeat the same values (such as 1, 1, 3, 5, 5), while the merge column in the other dataset will not have repeat values (such as 1, 3, 5). As you might have guessed, in a many-to-many join, both of your merge columns will have repeat values.

I believe a join will do it for you. Something like this:


Combining DataFrames with Pandas – Data Analysis and , Data Analysis and Visualization in Python for Ecologists Combine two DataFrames using a unique ID found in both DataFrames. to make sure we have the same columns and associated column format in both datasets. still get the same result # What's the size of the output data? merged_inner.shape merged_inner. Can either be column names or arrays with length equal to the length of the DataFrame. left_index − If True, use the index (row labels) from the left DataFrame as its join key(s). In case of a DataFrame with a MultiIndex (hierarchical), the number of levels must match the number of join keys from the right DataFrame.

Try with concat on the column axis:

combined = pandas.concat([df_low_rename, df_high_rename], axis=1)

Why And How To Use Merge With Pandas in Python, If you've been using Python in your work — especially for data “Merging” two datasets is the process of bringing two datasets together into one, and aligning the With the operation above, left_merge has the same size as� How To Compare Two Sets in Python. We’ll go over six set methods in pairs of two at a time. Each method will have a description, a visual diagram, and a code example. Let’s get to it.

Both data sets didn't have the same indexes. I fixed it like this:

df_low_rename = df_low_rename.reset_index(drop=True)
df_high_rename = df_high_rename.reset_index(drop=True)

Then I used the join function:

df_combined = df_low_rename.join(df_high_rename)

This way, I got the right output. Thanks to everyone who tried to help me and I apologize for this rookie mistake.

Merge, join, and concatenate — pandas 0.25.0.dev0+752.g49f33f0d , pandas provides various facilities for easily combining together Series or If you need to use the operation over several datasets, use a list comprehension. In [ 33]: result.index.levels Out[33]: FrozenList([['z', 'y', 'x', 'w'], [0, 1, 2, 3, 4, 5, 6, 7, 8, Can either be column names, index level names, or arrays with length equal to� Some of the most interesting studies of data come from combining different data sources. These operations can involve anything from very straightforward concatenation of two different datasets, to more complicated database-style joins and merges that correctly handle any overlaps between the datasets.

Python, Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labelled axes (rows and columns). A Data frame is a� The pd.merge() function recognizes that each DataFrame has an "employee" column, and automatically joins using this column as a key. The result of the merge is a new DataFrame that combines the information from the two inputs.

merge two data frame with different length, what about merge(df1, df2, by = "row.names", all = TRUE). That will add a new column to the resulting data frame containing the row names of� A merge_asof() is similar to an ordered left-join except that we match on nearest key rather than equal keys. For each row in the left DataFrame, we select the last row in the right DataFrame whose on key is less than the left’s key. Both DataFrames must be sorted by the key. Optionally an asof merge can perform a group-wise merge.

Combining Data in Pandas With merge(), .join(), and , With the two datasets loaded into DataFrame objects, you'll select a small slice of the precipitation dataset, and then use a plain merge() call to� Python | Merge two lists alternatively. Given two lists, write a Python program to merge the given lists in an alternative fashion, provided that the two lists are of equal length.

  • How does your df_low_rename and df_high_rename look like. More specifically how does their index look like ?
  • Both have been re-indexed. 11789 rows x 1 column
  • can you provide a few samples of each dataframe?e.g., print(df_low_rename.head().to_string())
  • If you could reproduce your issue with some mock datasets for people to try out and see it isn't just something with your data that could help. People have jumped to the current answers because they should work for this case.
  • @adrtam low_gdp \\ 0 2.39 \\ 1 3.49 \\ 2 2.85 \\ 3 1.50 \\ 4 0.62 \\
  • I get this error if I use merge: MergeError: No common columns to perform merge on
  • If you dont have any columns in common, add a new tmp column say df1['tmp']=1, df2['tmp']=1 then merge the data frames and drop the tmp column. Check this
  • Actually have tried this already. Exactly how you suggetted it, but also got one culumn with only NaN’s
  • did you check the data? a few sample would be helpful.
  • I did a lot of ‘cleaning’. No empty values or non-numerical values left. Just one column with 11789 rows containing only floats.
  • You may mark this as an answer, glad you found your solution!