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I have a data set that looks like this-

ID        Search    Provider
1           Yes     A
1           Yes     B
1           No      B
1           No      C
2           Yes     D
2           Yes     A
2           Yes     B
2           No      B
2           No      C

What I want to find out is if the Providers for Search==Yes is different from Search==No for a given ID. E.g.- 'ID 1', 'Search=No' goes to Providers B,C whereas 'Search=Yes' goes to Providers A,B. So the provider A is new for ID 1.

I know I can use the isin function to identify the unique values between two lists. However, how do I do it across multiple rows of ID and Search? And how do I compile the Provider values into lists for each subgroup defined by ID and Search? I guess I will need to use nested loops but not being able to write the code. Would really appreciate if someone can help me on this.

Rather than compiling into lists, you might want to consider sets. In a generic sense, this might be more useful as I would assume order and redundancy doesn't matter. Also, it's easier to determine which providers are in one set and not another. You can rearrange your dataframe with pivot_table to do this:

df_new = df.pivot_table(index='ID', columns='Search', aggfunc=set).droplevel(0, axis=1)


Search      No        Yes
1       {C, B}     {A, B}
2       {C, B}  {D, A, B}

With this new dataframe, you can compare values with the same 'ID' easiliy:

# df_new['No'] == df_new['Yes']   # If providers are the same between "yes" and "no"
df_new['Yes'] - df_new['No']      # Providers that are in "yes" but not "no"

Result (for set difference):

1       {A}
2    {D, A}
dtype: object

Pandas GroupBy: Your Guide to Grouping Data in Python – Real , ["last_name"] to specify the columns on which you want to perform the actual aggregation. You can pass a lot more than just a single column name to . Now I want to group this by two columns like following: Inserting data into a pandas dataframe and providing column name. Counting values on two columns

This can be done in a few steps. First, group by ID and search, then get the unique values with value_counts.

>>> df1 = df.groupby(['ID', 'Search']).Provider.value_counts()
ID  Search  Provider
1   No      B           1
            C           1
    Yes     A           1
            B           1
2   No      B           1
            C           1
    Yes     A           1
            B           1
            D           1

For each ID/Provider combination, you can then get a count of the number of Yes/No Searches

>>> df2 = df1.unstack(level='Search', fill_value=0)
Search       No  Yes
ID Provider         
1  A          0    1
   B          1    1
   C          1    0
2  A          0    1
   B          1    1
   C          1    0
   D          0    1

From here, you can get the list of Provider/ID combos that have either Yes or No but not both

>>> df2 = df1.query('Yes != No')
Search       No  Yes
ID Provider         
1  A          0    1
   C          1    0
2  A          0    1
   C          1    0
   D          0    1

Python, where() method is used to check a data frame for one or more condition and return the result accordingly. By default, The rows not satisfying the condition are filled with NaN value. id.1.value.1 id.2.value.2 id.1.question id.2.value.2 TRUE FALSE TRUE TRUE I want to create logic that scans the column names of the df and extracts the last number only from column names that have value in column name and compare the value in the cell of the column that contains value with following logic:

Method 1

You can use groupby.agg(set) first, then again groupby.diff:

dfg = df.groupby(['ID', 'Search']).agg(set).reset_index()

1       {A}
3    {A, D}
Name: Provider, dtype: object`

Method 2

Splitting the dataset up in yes and no then groupby.set:

yes = df.loc[df['Search'] == 'Yes']
no  = df.loc[df['Search'] == 'No']

yes_agg = yes.groupby('ID')['Provider'].agg(set)
no_agg = no.groupby('ID')['Provider'].agg(set)

# get the difference between the sets
yes_agg - no_agg

1       {A}
2    {A, D}
Name: Provider, dtype: object

String compare in pandas python – Test whether two strings are equal, Compare two strings in pandas dataframe – python (case sensitive); Compare two string 2. 3. 4. 5. 6. 7. 8. 9. import pandas as pd. import numpy as np. df1 = { Let's compare two columns to test whether they are equal. NA Drop Missing value in Pandas Python · Handling Missing values of column in pandas python  Pandas: Select rows that match a string less than 1 minute read Micro tutorial: Select rows of a Pandas DataFrame that match a (partial) string. import pandas as pd #create sample data data = {'model': ['Lisa', 'Lisa 2', 'Macintosh 128K', 'Macintosh 512K'], 'launched': [1983, 1984, 1984, 1984], 'discontinued': [1986, 1985, 1984, 1986]} df = pd.

pandas.DataFrame.equals, Compare two DataFrame objects of the same shape and return a DataFrame where types and values for their elements and column labels, which will return True. 2: [20]}) >>> exactly_equal 1 2 0 10 20 >>> df.equals(exactly_equal) True​. In this short guide, I’ll show you how to compare values in two Pandas DataFrames. I’ll also review how to compare values from two imported files. Steps to Compare Values in two Pandas DataFrames Step 1: Prepare the datasets to be compared. To start, let’s say that you have the following two datasets that you want to compare: First Dataset:

Working with text data, In [2]: pd.Series(['a', 'b', 'c'], dtype="string") Out[2]: 0 a 1 b 2 c dtype: string In [3]: pd​. Missing values in a StringArray will propagate in comparison operations, rather Since df.columns is an Index object, we can use the .str accessor Out[​48]: 0 oof 123 1 rab zab 2 <NA> dtype: string # Using regex groups In [49]: pat = r​"(? import pandas as pd df1 = pd.read_csv ('~/file1.csv',sep="\s+") df2 = pd.read_csv ('~/file2.csv',sep="\s+") Now data is loaded into two separate DataFrames which we are going to compare. Method read_csv has many options but default behavior is use first row as DataFrame column name and create automatic numeric index.

Group By: split-apply-combine, For DataFrame objects, a string indicating a column to be used to group. Of course pandas Index objects support duplicate values. If a non-unique index is​  String compare in pandas python is used to test whether two strings (two columns) are equal. In this example lets see how to Compare two strings in pandas dataframe – python (case sensitive) Compare two string columns in pandas dataframe – python (case insensitive)