Merge is a function in the pandas namespace, and it is also available as a DataFrame instance method merge , with the calling DataFrame being implicitly considered the left object in the join. The related join method, uses merge internally for the index-on-index (by default) and column (s)-on-index join. In Python’s Pandas Library Dataframe class provides a function to merge Dataframes i.e. DataFrame.merge(right, how='inner', on=None, lefton=None, righton=None, leftindex=False, rightindex=False, sort=False, suffixes=('x', 'y'), copy=True, indicator=False, validate=None) It accepts a hell lot of arguments. In this article, you’ll learn how multiple DataFrames could be merged in python using Pandas library. Merging DataFrames is the core process to start with data analysis and machine learning tasks.

Datacamp course notes on merging dataset with pandas.

Reading Multiple Files

pandas provides the following tools for loading in datasets:

  • pd.read_csv for CSV files
    • dataframe = pd.read_csv(filepath)
    • dozens of optional input parameters
  • Other data import tools:
    • pd.read_excel()
    • pd.read_html()
    • pd.read_json()

To reading multiple data files, we can use a for loop:

Or simply a list comprehension:

Or using glob to load in files with similar names:
glob() will create a iterable object: ‘filenames’, containing all matching filenames in the current directory.

Another example:

Reindexing DataFrames

The index is a privileged column in Pandas providing convenient access to Series or DataFrame rows.
‘indexes’ vs. ‘indices’

  • indices: many index labels within a index data structure
  • indexes: many pandas index data structures.

We can access the index directly by .index attribute. To reindex a dataframe, we can use .reindex():

Note that here we can also use other dataframe’s index to reindex the current dataframe. If there are indices that do not exist in the current dataframe, the row will show NaN, which can be dropped via .dropna() eaisly. This is normally the first step after merging the dataframes. Also, we can use forward-fill or backward-fill to fill in the Nas by chaining .ffill() or .bfill() after the reindexing.

To sort the index in alphabetical order, we can use .sort_index() and .sort_index(ascending = False).

Pandas

To sort the dataframe using the values of a certain column, we can use .sort_values('colname')

Arithmetic with Series & DataFrames

Scalar Mutiplication

Divide()

If we want to get the max and the min temperature column all divided by the mean temperature column

Here, we cannot directly divide the week1_range by week1_mean, which will confuse python. Instead, we use .divide() to perform this operation.

This will broadcast the series week1_mean values across each row to produce the desired ratios.

To compute the percentage change along a time series, we can subtract the previous day’s value from the current day’s value and dividing by the previous day’s value. The .pct_change() method does precisely this computation for us.

Add()

How arithmetic operations work between distinct Series or DataFrames with non-aligned indexes? When we add two panda Series, the index of the sum is the union of the row indices from the original two Series. Arithmetic operations between Panda Series are carried out for rows with common index values. If the indices are not in one of the two dataframe, the row will have NaN.

Tips:
To replace a certain string in the column name:

Multiply()

In this exercise, stock prices in US Dollars for the S&P 500 in 2015 have been obtained from Yahoo Finance.

Using the daily exchange rate to Pounds Sterling, your task is to convert both the Open and Close column prices.

Appending and Concatenating Series

We can also stack Series on top of one anothe by appending and concatenating using .append() and pd.concat().

.append()

  • .append() is a Series and DataFrame method
  • syntax: s1.append(s2). Stacks rows of s2 below s1
  • Stacks rows without adjusting index values by default.
    • To discard the old index when appending, we can chain .reset_index(drop = True) after appending,

concat()

  • concat() is a pandas module function, and accepts a list or sequance of several Series or DataFrames to concatenate.
  • symtax: pd.concat([s1, s2, s3])
  • While the .append() method can only stack vertically (or row_wise), the function concat() is more flexible, and can concatenate both vertically and horizontally.
    • axis = 'rows' stacks vertically, axis = 'columns' stacks horizontally
  • Concat without adjusting index values by default.
    • To discard the old index when appending, we can specify argument ignore_index = True in the funtion.

When stacking multiple Series, pd.concat() is in fact equivalent to chaining method calls to .append()
result1 = pd.concat([s1, s2, s3]) = result2 = s1.append(s2).append(s3)

Append then concat

Appending and Concatenating DataFrames

df1.append(df2)

  • By default, the dataframes are stacked row-wise (vertically).
    • If the two dataframes have identical index names and column names, then the appended result would also display identical index and column names.
    • If the two dataframes have different index and column names:
      • If there is a index that exist in both dataframes, there will be two rows of this particular index, one shows the original value in df1, one in df2. Different columns are unioned into one table. NaNs are filled into the values that come from the other dataframe.

pd.concat([df1, df2])

  • By default, the dataframes are stacked row-wise (vertically).
    • If the two dataframes have identical index names and column names, then the appended result would also display identical index and column names.
  • If we use pd.concat([df1, df2], axis = 1) or pd.concat([df1, df2], axis = 'columns') stacks dataframe columns horizontally on the right.
    • If there is a index that exist in both dataframes, the row will get populated with values from both dataframes when concatenating.

Example: Reading multiple files to build a DataFrame.
It is often convenient to build a large DataFrame by parsing many files as DataFrames and concatenating them all at once. You’ll do this here with three files, but, in principle, this approach can be used to combine data from dozens or hundreds of files.

The expression '%s_top5.csv' % medal evaluates as a string with the value of medal replacing %s in the format string.

Concatenation, Keys & MultiIndexes

In order to differentiate data from different dataframe but with same column names and index:

  • we can use keys to create a multilevel index. The order of the list of keys should match the order of the list of dataframe when concatenating.

  • or we can concat the columns to the right of the dataframe with argument axis = 1 or axis = columns. To avoid repeated column indices, again we need to specify keys to create a multi-level column index.

or use a dictionary instead. In that case, the dictionary keys are automatically treated as values for the keys in building a multi-index on the columns.

Dataframes

Another example:

Outer & Inner Joins

We can stack dataframes vertically using append(), and stack dataframes either vertically or horizontally using pd.concat(). pd.concat() is also able to align dataframes cleverly with respect to their indexes.

A ValueError exception is raised when the arrays have different size along the concatenation axis

Joining tables involves meaningfully gluing indexed rows together.
Note: we don’t need to specify the join-on column here, since concatenation refers to the index directly

  • Outer join preserves the indices in the original tables filling null values for missing rows.
    • Union of index sets (all labels, no repetition)
    • Missing fields filled with NaN
  • Inner join has only index labels common to both tables
    • Intersection of index sets

Very often, we need to combine DataFrames either along multiple columns or along columns other than the index, where merging will be used.

merge() function extends concat() with the ability to align rows using multiple columns.

Inner join

Pandas
  • Merge all columns that occur in both dataframes: pd.merge(population, cities). It performs inner join, which glues together only rows that match in the joining column of BOTH dataframes.

  • Merge on a particular column or columns that occur in both dataframes: pd.merge(bronze, gold, on = ['NOC', 'country']).
    We can further tailor the column names with suffixes = ['_bronze', '_gold'] to replace the suffixed _x and _y

  • When the columns to join on have different labels: pd.merge(counties, cities, left_on = 'CITY NAME', right_on = 'City'). This way, both columns used to join on will be retained.

Left join & Right join

It keeps all rows of the left dataframe in the merged dataframe.

  • For rows in the left dataframe with matches in the right dataframe, non-joining columns of right dataframe are appended to left dataframe.
  • For rows in the left dataframe with no matches in the right dataframe, non-joining columns are filled with nulls.

And vice versa for right join.

Outer join

Outer join is a union of all rows from the left and right dataframes.

Besides using pd.merge(), we can also use pandas built-in method .join() to join datasets.

Which merging/joining method should we use?

The simpler the better.

  • To stack two Series or DataFrames vertically: df1.append(df2)
  • To stack many horizontally or vertically, or perform simple inner/outer joins on indexes: pd.concat([df1, df2])
  • To perform simple left/right/inner/outer joins on indexes: df1.join(df2)
  • To perform many joins on multiple columns: pd.merge([df1, df2])

Ordered merges

We often want to merge dataframes whose columns have natural orderings, like date-time columns.

merge_ordered()

pd.merge_ordered() can join two datasets with respect to their original order. The merged dataframe has rows sorted lexicographically accoridng to the column ordering in the input dataframes. By default, it performs outer-join

To distinguish data from different orgins, we can specify suffixes in the arguments.

merge_ordered() can also perform forward-filling for missing values in the merged dataframe. Note: ffill is not that useful for missing values at the beginning of the dataframe

merge_asof()??

Similar to pd.merge_ordered(), the pd.merge_asof() function will also merge values in order using the on column, but for each row in the left DataFrame, only rows from the right DataFrame whose 'on' column values are less than the left value will be kept.

This function can be use to align disparate datetime frequencies without having to first resample.

Here, you’ll merge monthly oil prices (US dollars) into a full automobile fuel efficiency dataset. The oil and automobile DataFrames have been pre-loaded as oil and auto. The first 5 rows of each have been printed in the IPython Shell for you to explore.

These datasets will align such that the first price of the year will be broadcast into the rows of the automobiles DataFrame. This is considered correct since by the start of any given year, most automobiles for that year will have already been manufactured.

You have a sequence of files summer_1896.csv, summer_1900.csv, …, summer_2008.csv, one for each Olympic edition (year).

You will build up a dictionary medals_dict with the Olympic editions (years) as keys and DataFrames as values.

The dictionary is built up inside a loop over the year of each Olympic edition (from the Index of editions).

Once the dictionary of DataFrames is built up, you will combine the DataFrames using pd.concat().

Counting medals by country/edition in a pivot table

Computing fraction of medals per Olympic edition and the percentage change in fraction of medals won

Expanding Windows

A common alternative to rolling statistics is to use an expanding window, which yields the value of the statistic with all the data available up to that point in time.

These follow a similar interface to .rolling, with the .expanding method returning an Expanding object.

As these calculations are a special case of rolling statistics, they are implemented in pandas such that the following two calls are equivalent:

To see if there is a host country advantage, you first want to see how the fraction of medals won changes from edition to edition.

The expanding mean provides a way to see this down each column. It is the value of the mean with all the data available up to that point in time.

Reshaping for analysis

Visualization

In this article we will discuss how to merge different Dataframes into a single Dataframe using Pandas Dataframe.merge() function. Merging is a big topic, so in this part we will focus on merging dataframes using common columns as Join Key and joining using Inner Join, Right Join, Left Join and Outer Join.

Dataframe.merge()

In Python’s Pandas Library Dataframe class provides a function to merge Dataframes i.e.
It accepts a hell lot of arguments. Let’s discuss some of them,
Imp Arguments :

  • right : A dataframe or series to be merged with calling dataframe
  • how : Merge type, values are : left, right, outer, inner. Default is ‘inner’. If both dataframes has some different columns, then based on this value, it will be decided which columns will be in the merged dataframe.
  • on : Column name on which merge will be done. If not provided then merged on indexes.
  • left_on : Specific column names in left dataframe, on which merge will be done.
  • right_on : Specific column names in right dataframe, on which merge will be done.
  • left_index : bool (default False)
    • If True will choose index from left dataframe as join key.
  • right_index : bool (default False)
    • If True will choose index from right dataframe as join key.
  • suffixes : tuple of (str, str), default (‘_x’, ‘_y’)
    • Suffex to be applied on overlapping columns in left & right dataframes respectively.

Well these are a lot of arguments and things seems over engineered here. So, let’s discuss each details be small examples one by one.

First of all, let’s create two dataframes to be merged.

Dataframe 1:
This dataframe contains the details of the employees like, ID, name, city, experience & Age i.e.
Contents of the first dataframe empDfObj created are,
Dataframe 2:
This dataframe contains the details of the employees like, ID, salary, bonus and experience i.e.
Contents of the second dataframe created are,
Now let’s see different ways to merge these two dataframes,

Merge DataFrames on common columns (Default Inner Join)

In both the Dataframes we have 2 common column names i.e. ‘ID’ & ‘Experience’. If we directly call Dataframe.merge() on these two Dataframes, without any additional arguments, then it will merge the columns of the both the dataframes by considering common columns as Join Keys i.e. ‘ID’ & ‘Experience’ in our case. So, basically columns from both the dataframes will be merged for the rows in which values of ‘ID’ & ‘Experience’ are same i.e.
Merged Dataframe mergedDf contents are:
It merged the contents of the unique columns (salary & bonus) from dataframe 2 with the columns of dataframe 1 based on ‘ID’ & ‘Experience’ columns. Because if we don’t provide the column names on which we want to merge the two dataframes then it by defaults merge on columns with common names. Like, in our case it was ‘ID’ & ‘Experience’.

Merge

Pandas Dataframe Merge Join

Also, we didn’t provided the ‘how’ argument in merge() call. Default value of ‘how’ is ‘inner’. It means dataframes are merged like INNER JOIN in Databases.

What is Inner Join ?

While Merging or Joining on columns (keys) in two Dataframes. Include only rows from Left & Right dataframes which have same values in key columns.

In above example key columns on which inner join happened were ‘ID’ & ‘Experience’ columns. So, during inner join only those rows are picked in merged dataframe for which values of ‘ID’ & ‘Experience’ columns are same in 2 dataframes. So basically by default Inner Join was done by using intersection of keys in both the dataframes.

Results will be same if we explicitly pass ‘how’ argument with value ‘inner’ i.e.

Merge Dataframes using Left Join

What is left join ?

While Merging or Joining on columns (keys) in two Dataframes. Include all rows from Left dataframe and add NaN for values which are
missing in right dataframe for those keys.

In above example if we will pass how argument with value ‘left’ then it will merge two dataframes using left join i.e.
Contents of the merged dataframe :
We can see that it picked all rows from left dataframe and there is no row with ‘ID’ 17 and ‘Experience’ 11 in right dataframe. Therefore for that row values of unique Columns from right dataframe (Salary and Bonus) are NaN in merged dataframe.

Merge DataFrames using Right Join

What is Right join ?

While Merging or Joining on columns (keys) in two Dataframes. Include all rows from Right dataframe and add NaN for values which are
missing in Left dataframe for those keys.

In above example if we will pass how argument with value ‘right’ then it will merge two dataframes using Right Join i.e.
Contents of the merged dataframe :
We can see that it picked all rows from right dataframe and there is no row with ID 21 and Experience 10 in left dataframe. Therefore for that row values of unique Columns from left dataframe (i.e. Name, Age, City) are NaN in merged dataframe.

Merge DataFrames using Outer Join

What is Outer join ?

Dataframes

While Merging or Joining on columns (keys) in two Dataframes. Include all rows from Right and Left dataframes and add NaN for values which are missing in either Left or Right dataframe for any key.

In above example if we will pass how argument with value ‘outer’ then it will merge two dataframes using Outer Join i.e.
Contents of the merged dataframe :
We can see that it picked all rows from right & left dataframes and there is no row with,

Pandas Merge Large Dataframes &

  • ID 21 and Experience 10 in left dataframe
  • ID 17 and Experience 11 in right dataframe

Pandas Merge Large Dataframes Pdf

Therefore for that row NaN is added for missing values in merged dataframe.

Complete example is as follows,
Output:

Related Posts:

Coments are closed

Most Viewed Posts

  • Rustafied
  • Breathe Amazon Prime
  • Tor Browser Portable 2019
  • 4chan Discord
  • Microsoft Teams Electron

Scroll to top