CODEWITHSUNDEEP
pythonpandasloopsdataframe
Rocky
Rocky

Reputation: 141

Iterate over rows of Pandas dataframe

I have df like:

CELLID  lon       lat         METER           LATITUDE_SM LONGITUDE_SM  Path_ID
2557709 5.286339 51.353820 E0047000004028217  51.3501      5.3125    2557709_E0047000004028217

For each Path_ID(str) I would like to iterate the loop and would like to achieve df1 like:

Path_ID                    METER          LATITUDE_SM  LONGITUDE_SM
2557709_E0047000004028217 E0047000004028217  51.3501    5.3125 
Path_ID                   CELLID            Lon           lat
2557709_E0047000004028217 2557709         5.286339     51.353820

I have many rows in the df. I am doing something like 

for row in df.iterrows():
    print row ['Path_ID'],row['METER'],row['LATITUDE_SM'], row ['LONGITUDE_SM']

Upvotes: 1

Views: 6387

Answers (2)

rafaelc
rafaelc

Reputation: 59284

It is very hard to understand your goal, but IIUC, you want to group by Path_ID and print each value

grouped_df= df.groupby("Path_ID")[["Path_ID", "METER", "LATITUDE_SM", "LONGITUDE_SM"]]
for key, val in grouped_df:
    print grouped_df.get_group(key), "\n"

Output

                     Path_ID              METER  LATITUDE_SM  LONGITUDE_SM
0  2557709_E0047000004028217  E0047000004028217        51.35        5.3125

Upvotes: 2

jpp
jpp

Reputation: 164823

It is unclear why you want this behaviour, but you can achieve this with pd.DataFrame.iloc.

If you only need specific columns, replace : with a list of column numbers.

import pandas as pd, numpy as np

df = pd.DataFrame(np.random.random((5, 5)))

for i in range(len(df.index)):
    print(df.iloc[[i], :])

#           0         1         2         3         4
# 0  0.587349  0.947435  0.974285  0.498303  0.135898
#           0         1         2         3         4
# 1  0.292748  0.880276  0.522478  0.081902  0.187494
#           0         1         2         3         4
# 2  0.692022  0.908397  0.200202  0.099722  0.348589
#           0         1         2         3         4
# 3  0.041564  0.980425  0.899634  0.725757  0.569983
#           0         1         2         3         4
# 4  0.787038  0.000077  0.213646  0.444095  0.022923

Upvotes: 2

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