How to count the same rows between multiple CSV files in Pandas?

Question

I merged 3 different CSV(D1,D2,D3) Netflow datasets and created one big dataset(df), and applied KMeans clustering to this dataset. To merge them I did not use pd.concat because of memory error and solved with Linux terminal.

df = pd.read_csv('D.csv')
#D is already created in a Linux machine from terminal

........
KMeans Clustering
........

As a result of clustering, I separated the clusters into a dataframe
then created a csv file.
cluster_0 = df[df['clusters'] == 0]
cluster_1 = df[df['clusters'] == 1]
cluster_2 = df[df['clusters'] == 2]

cluster_0.to_csv('cluster_0.csv')
cluster_1.to_csv('cluster_1.csv')
cluster_2.to_csv('cluster_2.csv')

#My goal is to understand the number of same rows with clusters
#and D1-D2-D3
D1 = pd.read_csv('D1.csv')
D2 = pd.read_csv('D2.csv')
D3 = pd.read_csv('D3.csv')

All these datasets contain the same column names, they have 12 columns(all numerical values)

Example expected result:

cluster_0 has xxxx numbers of same rows from D1, xxxxx numbers of same rows from D2, xxxxx numbers of same rows from D3?

Answer 1

cluster0_D1 = pd.merge(D1, cluster_0, how ='inner')
number_of_rows_D1 = len(cluster0_D1)

cluster0_D2 = pd.merge(D2, cluster_0, how ='inner')
number_of_rows_D2 = len(cluster0_D2)

cluster0_D3 = pd.merge(D3, cluster_0, how ='inner')
number_of_rows_D3 = len(cluster0_D3)

print("How many samples belong to D1, D2, D3 for cluster_0?")
print("D1: ",number_of_rows_D1)
print("D2: ",number_of_rows_D2)
print("D3: ",number_of_rows_D3)

I think this solved my problem.

Answer 2

I do not believe there is enough information in the question to cover edge cases, but this shoul work if I understand correctly.

# Read in the 3, and add a column called "file" so we know which file they came from
D1 = pd.read_csv('D1.csv')
D1['file'] = 'D1.csv'
D2 = pd.read_csv('D2.csv')
D2['file'] = 'D2.csv'
D3 = pd.read_csv('D3.csv')
D3['file'] = 'D3.csv'

# Merge them together into the DF that the "awk" command was doing
df = pd.concat([D1, D2, D3], axis=1)

# Save off the series showing which files each row belong sto
files = df['file']
# Drop it so that doesnt get included in your analysis
df.drop('file', inplace=True, axis=1)

"""
There is no code in the question to show the KMeans clustering
"""

# Add the filename back
df['filename'] = files

We will avoid using the awk command and instead opt for pd.concat.