can i set dataframe values without using iterrows()?

The rows you get back from iterrows are copies that are no longer connected to the original data frame, so edits don't change your dataframe. Thankfully, because each item you get back from iterrows contains the current index, you can use that to access and edit the relevant row of the dataframe:

for index, row in rche_df.iterrows():
   if isinstance(row.wgs1984_latitude, float):
   row = row.copy()
target = row.address_chi
dict_temp = geocoding(target)
rche_df.loc[index, 'wgs1984_latitude'] = dict_temp['lat']
rche_df.loc[index, 'wgs1984_longitude'] = dict_temp['long']

Another way based on this question:

for index, row in rche_df.iterrows():
   if isinstance(row.wgs1984_latitude, float):
   row = row.copy()
target = row.address_chi
dict_temp = geocoding(target)

rche_df.at[index, 'wgs1984_latitude'] = dict_temp['lat']
rche_df.at[index, 'wgs1984_longitude'] = dict_temp['long']

Suggestion : 2

5 days ago An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values. See also DataFrame.iterrows , 4 days ago Sep 20, 2020 · Pandas DataFrame.iterrows () is used to iterate over a pandas Data frame rows in the form of (index, series) pair. This function iterates over the data frame column, it will return a tuple with the column name and content in form of series. Syntax: DataFrame.iterrows () Yields: index- The index of the row. A tuple for a MultiIndex data- The data of the row as a Series … , 2 days ago Definition and Usage. The iterrows () method generates an iterator object of the DataFrame, allowing us to iterate each row in the DataFrame. Each iteration produces an index object and a row object (a Pandas Series object). , 4 days ago Oct 01, 2021 · import pandas as pd df = pd.DataFrame ( [ ['Banana', 'Cherry', 'Grapes', 'Oranges', 'Apple', 'litchi']]) new_val = next (df.iterrows ()) [1] print (new_val) In the above program, we iterate over the Pandas DataFrame and having no column name by using the iterrows () method.

In[2]: import pandas as pd...: ...: # Original DataSet...: d = {
   'A': [1, 1, 1, 1, 2, 2, 2, 2, 3],
   ...: 'B': ['a', 'a', 'a', 'x', 'b', 'b', 'b', 'x', 'c'],
   ...: 'C': [11, 22, 33, 44, 55, 66, 77, 88, 99],
}...: ...: df = pd.DataFrame(d)...: df Out[2]: A B C 0 1 a 11 1 1 a 22 2 1 a 33 3 1 x 44 4 2 b 55 5 2 b 66 6 2 b 77 7 2 x 88 8 3 c 99

df.C[df.B == 'x'] = df.C.shift(-1)

In[2]: import pandas as pd...: ...: # Original DataSet...: d = {
   'A': [1, 1, 1, 1, 2, 2, 2, 2, 3],
   ...: 'B': ['a', 'a', 'a', 'x', 'b', 'b', 'b', 'x', 'c'],
   ...: 'C': [11, 22, 33, 44, 55, 66, 77, 88, 99],
}...: ...: df = pd.DataFrame(d)...: df Out[2]: A B C 0 1 a 11 1 1 a 22 2 1 a 33 3 1 x 44 4 2 b 55 5 2 b 66 6 2 b 77 7 2 x 88 8 3 c 99

Out[3]: A B C 0 1 a 11 1 1 a 22 2 1 a 33 3 1 x 55 4 2 b 55 5 2 b 66 6 2 b 77 7 2 x 99 8 3 c 99

# Code that produces the above outcome
for idx, x_row in df[df['B'] == 'x'].iterrows(): df.loc[idx, 'C'] = df.loc[idx + 1, 'C'] df

df.C[df.B == 'x'] = df.C.shift(-1)

Suggestion : 3

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,,Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example, >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float']) >>> row = next(df.iterrows())[1] >>> row int 1.0 float 1.5 Name: 0, dtype: float64 >>> print(row['int'].dtype) float64 >>> print(df['int'].dtype) int64 To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows. ,To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.,You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

>>> df = pd.DataFrame([
      [1, 1.5]
   ], columns = ['int', 'float']) >>>
   row = next(df.iterrows())[1] >>>
   row
int 1.0
float 1.5
Name: 0, dtype: float64 >>>
   print(row['int'].dtype)
float64
   >>>
   print(df['int'].dtype)
int64

Suggestion : 4

DataFrame − column labels,Python Pandas - DataFrame,To iterate over the rows of the DataFrame, we can use the following functions −,Iterating a DataFrame gives column names. Let us consider the following example to understand the same.

1._

import pandas as pd
import numpy as np

N = 20
df = pd.DataFrame({
   'A': pd.date_range(start = '2016-01-01', periods = N, freq = 'D'),
   'x': np.linspace(0, stop = N - 1, num = N),
   'y': np.random.rand(N),
   'C': np.random.choice(['Low', 'Medium', 'High'], N).tolist(),
   'D': np.random.normal(100, 10, size = (N)).tolist()
})

for col in df:
   print col

Its output is as follows −

A
C
D
x
y

3._

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4, 3), columns = ['col1', 'col2', 'col3'])
for key, value in df.iteritems():
   print key, value

7._

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4, 3), columns = ['col1', 'col2', 'col3'])
for row in df.itertuples():
   print row

9._

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4, 3), columns = ['col1', 'col2', 'col3'])

for index, row in df.iterrows():
   row['a'] = 10
print df