Setting a value to a cell in a pandas dataframe

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I have the following pandas dataframe:

K = pd.DataFrame({"A":[1,2,3,4], "B":[5,6,7,8]})

Then I set the cell in the first row and first column to 11:

K.iloc[0]["A"] = 11

And when I check the dataframe again, I see that the value assignment is done and K.iloc[0]["A"] is equal to 11. However when I add a column to this data frame and do the same operation for a cell in the new column, the value assignment is not successful:

K["C"] = 0
K.iloc[0]["C"] = 11

So, when I check the dataframe again, the value of K.iloc[0]["C"] is still zero. I appreciate if somebody can tell me what is going on here and how I can resolve this issue.

When you use K.iloc[0]["C"], you first take the first line, so you have a copy of a slice from your dataframe, then you take the column C. So you change the copy from the slice, not the original dataframe.

That your first call, K.iloc[0]["A"] = 11 worked fine was in some sens a luck.

The good habit is to use loc in "one shot", so you have access to the original value of the dataframe, not on a slice copy :

K.loc[0,"C"] = 11

Be careful that iloc and loc are different function, even if they seems quite similar here.

Set value for particular cell in pandas DataFrame with iloc, For mixed position and index, use .ix . BUT you need to make sure that your index is not of integer, otherwise it will cause confusions. df.ix[0� loc and iloc behave the same whenever your dataframe has an integer index starting at 0 Set value to cell For setting an individual value, you must use.loc. In other words, assign a value to a specific cell in a dataframe.

For simplicity, I would do the operations in a different order and use loc:

K.loc[0, 'C'] = 0
K.loc[0, ['A', 'C']] = 11

Pandas how to get a cell value and update it, Dataframe cell value by Integer position iat - Access a single value for a row/column pair by integer position. Use iat if you only need to get or set a single value in a DataFrame or Series. Extending Jianxun's answer, using set_value mehtod in pandas. It sets value for a column at given index. From pandas documentations: DataFrame.set_value (index, col, value) To set value at particular index for a column, do: df.set_value(index, 'COL_NAME', x) Hope it helps. share. Share a link to this answer.

If default index, RangeIndex is possible use DataFrame.loc, but it set index values by label 0 (what is same like position 0):

K['C'] = 0
K.loc[0, ["A", "C"]] = 11
print (K)
    A  B   C
0  11  5  11
1   2  6   0
2   3  7   0
3   4  8   0

Reason why your solution failed is possible find in docs:

This can work at times, but it is not guaranteed to, and therefore should be avoided:

dfc['A'][0] = 111

Solution with DataFrame.iloc is possible with get positions of columns by Index.get_indexer:

print (K.columns.get_indexer(["A", "C"]))
[0 2]

K['C'] = 0
K.iloc[0, K.columns.get_indexer(["A", "C"])] = 11
print (K)
    A  B   C
0  11  5  11
1   2  6   0
2   3  7   0
3   4  8   0

Pandas Indexing Examples: Accessing and Setting Values on , Access a single value for a row/column label pair. Similar to loc , in that both provide label-based lookups. Use at if you only need to get or set a single value in a� import pandas as pd df = pd.DataFrame ( [ [ 30, 20, 'Hello' ], [ None, 50, 'foo' ], [ 10, 30, 'poo' ]], columns= [ 'A', 'B', 'C' ]) df. Let’s access cell value of (2,1) i.e index 2 and Column B.[2,'B'] 30. Value 30 is the output when you execute the above line of code.

loc should work :

K.loc[0]['C'] = 11
K.loc[0, 'C'] = 11

Both the above versions of loc will be able to assign values to the dataframe K. — pandas 1.1.1 documentation, when using[row_num, "Col B"] = "yes": the at() method added a new column in my data frame instead of modifying the. Col B of my� A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead If I do. In [3]: df['industry'] = 'yyy' I got the same warning message. Any ideas? Working with Python 3.5.2 and pandas 0.18.1.

How to change/update cell value in Python Pandas dataframe , Not sure about older version of pandas, but in 0.16 the value of a particular cell can be set based on multiple column values. Extending the answer provided by @waitingkuo, the same operation can also be done based on values of multiple columns.

You can now use to set by label, and DataFrame.iat to set by integer position. Setting Cell Values with at / iat # Setup df = pd.DataFrame({'A': [12, 23], 'B': [['a', 'b'], ['c', 'd']]}) df A B 0 12 [a, b] 1 23 [c, d] df.dtypes A int64 B object dtype: object

For pandas 0.10, where iloc is unavalable, filter a DF and get the first row data for the column VALUE: df_filt = df[df['C1'] == C1val & df['C2'] == C2val] result = df_filt.get_value(df_filt.index,'VALUE') if there is more then 1 row filtered, obtain the first row value. There will be an exception if the filter result in empty data frame.