Compare 2 Dataframe and Find the Matching Rows

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I have a master Dataframe as:

Time          Frq     Seq
12:46:17      4200.0  30700.0
12:49:29      4160.0  30690.0
12:46:18      3060.0  30700.0
12:46:18      3060.0  30700.0
12:46:19      3060.0  30700.0
12:46:20      3060.0  30700.0
12:46:20      4240.0  30700.0
12:46:19      4220.0  30700.0
12:46:18      4200.0  30700.0
12:46:18      4200.0  30700.0
12:46:38      3060.0  30700.0
12:43:04      4620.0  30701.0
12:46:38      4600.0  30701.0
12:46:05      4600.0  30701.0
12:46:37      3060.0  30700.0
12:48:35      3020.0  30690.0

and a child Dataframe as:

Frq     Seq
3060.0  30700.0
4600.0  30701.0

I want to collect the 1st occurrence of the Child Dataframe rows in Master Dataframe.

I want my Result Dataframe as:

Time          Frq     Seq
12:46:18      3060.0  30700.0
12:46:38      4600.0  30701.0

First remove duplicated in master by DataFrame.drop_duplicates and then use DataFrame.merge - if omit parameter on it merge by intersection of columns names between both DataFrames:

df = master.drop_duplicates(['Frq','Seq']).merge(child)
print (df)
       Time     Frq      Seq
0  12:46:18  3060.0  30700.0
1  12:46:38  4600.0  30701.0

Compare Python Pandas DataFrames for matching rows, How do you find the difference between two columns in pandas? Checking if any row (all columns) from another dataframe (df2) are present in df1 is equivalent to determining the intersection of the the two dataframes. This can be accomplished using the following function: pd.merge(df1, df2, on= ['A', 'B', 'C', 'D'], how='inner') For example, if df1 was.

my approach would be grouping to get the firsts of each group to then index for the elements in your child dataframe:

first = master.groupby('Frq', as_index=False).first()
first[first.Frq.isin(child.Frq)]

#       Frq      Time      Seq
# 1  3060.0  12:46:18  30700.0
# 6  4600.0  12:46:38  30701.0

How to Compare Values in two Pandas DataFrames, One possible solution to your problem would be to use merge. Checking if any row (all columns) from another dataframe (df2) are present in df1  Using the merge function you can get the matching rows between the two dataframes. So we are merging dataframe (df1) with dataframe (df2) and Type of merge to be performed is inner, which use intersection of keys from both frames, similar to a SQL inner join. df = df1.merge (df2, how = 'inner' ,indicator=False) df. 1.

Pandas Difference Between two Dataframes, Run the code in Python, and you'll get these two DataFrames: 'True', 'False') #​create new column in df1 to check if prices match print (df1) When you compare two DataFrames, you must ensure that the number of records in the first​  How np.where compare work in Pandas: Go row by row for example row 0; Check selected values: df1.value <= df2.low check 98 <= 97; Return the result as Series of Boolean values 4. False, False, True; Compare one column from first against two from second DataFrame. Comparing more than one column is frequent operation and Numpy/Pandas make this very easy and intuitive operation.

How do I compare columns in different data frames?, Using the merge function you can get the matching rows between the two dataframes. So we are merging dataframe(df1) with dataframe(df2) and Type of merge to be performed is inner, which use intersection of keys from both frames, similar to a SQL inner join. I posted a question over on StackOverflow on an efficient way of comparing two data frames with the same column structure, but with different rows. What I would like to end up with is an n x m logical matrix where n and m are the number of rows in the first and second data frames, respectively; and the value at the ith row and jth column indicates whether all the values from row i from data

compare different pandas dataframes to find matching rows, Using set, get unique values in each column. The intersection of these two sets will provide the unique values in both the columns. Example: df1 = pd.DataFrame​({'  When you compare two DataFrames, you must ensure that the number of records in the first DataFrame matches with the number of records in the second DataFrame. In our example, each of the two DataFrames had 4 records, with 4 products and 4 prices.

How to compare two dataframes and extract the matching entries as , Here is what we start with. In [16]: df1 Out[16]: name year 0 ram 1873 1 rob 1900 In [17]: df2 Out[17]: name text 0 ram A good kid 1 ram He was born on 1873 2  Python Pandas : How to Drop rows in DataFrame by conditions on column values; Pandas : Sort a DataFrame based on column names or row index labels using Dataframe.sort_index() Python Pandas : How to add new columns in a dataFrame using [] or dataframe.assign() Pandas : How to create an empty DataFrame and append rows & columns to it in python

Comments
  • if time col is sorted, can you try master.merge(child,on='Frq',suffixes=('','_')).groupby('Frq',as_index=False).first().reindex(master.columns,axis=1) ?