How to Get a Value from a Cell of a Dataframe

How to extract a cell value from a Dataframe in Pandas

You can use boolean indexing with DataFrame.loc for filter by condition and by column name:

s = df.loc[df['instrument_token'].eq(12295682), 'tradingsymbol']
#alternative
s = df.loc[df['instrument_token'] == 12295682, 'tradingsymbol']

And then get first value of Series:

a = s.iat[0]
a = s.iloc[0]
a = s.tolist()[0]
a = s.to_array()[0]
#general solution if not match condition and select first value failed
a = next(iter(s), 'no match')

Another idea is use DataFrame.set_index fo index by column instrument_token:

df = df.set_index('instrument_token')

Anf then select by DataFrame.loc or
DataFrame.at:

a = df.loc[12295682, 'tradingsymbol']
a = df.at[12295682, 'tradingsymbol']

How to get a value from a cell of a dataframe by position or label?

First thing's first. ix is deprecated, but ix allowed you to mix labels and indexers, and made a lot of guesses about what it was passed.

In today's day and age (with the current stable release being v0.22), ix is deprecated, so stick to explicit label or positional based indexers: loc for label based slicing, iloc for index based slicing; at for label based item access, and iat for index based item access.

If you know what your labels are, use at to access a single item -

df.at['Header I, II and III; x', df.columns[-1]]

If you know the position, use iat -

df.iat[2, -1]

In general, use *at when you want to access a single element, and *loc when you want to access a row/column slice.

How to get the content of a cell in a pandas dataframe

What about :

print(data["Monkey"]["Name"].values[0])

As previously, you extract the row/column you want, based on the row's index and column name. Then .values turn the result dataframe into a numpy arrays of values. Finally, just retrieve the first (and only) element

You might also be able to use :

print(data["Monkey"].iloc[0]["Name"])

Get t-1 value (from previous cell) in a pandas dataframe

Use shift on the columns to compute:

cols = ["Austria", "Belgium", "France"]
df[cols] = df[cols].shift()
print(df)

Output

    Timestamp  Austria  Belgium  France
1  1993-11-01      NaN      NaN     NaN
2  1993-11-02     6.11     7.01    7.69
3  1993-11-03     6.18     7.05    7.61
4  1993-11-04     6.17     7.20    7.67
5  1993-11-15     6.17     7.50    7.91

As an alternative:

df.iloc[:, 1:] = df.iloc[:, 1:].shift()
print(df)

Dealing with multiple values in Pandas Dataframe Cell

Use:

#unpivot by melt
df = df.melt('solution_id')
#create lists by split #
df['value'] = df['value'].str.split('#')
#repeat rows by value column
df = df.explode('value')
#counter for new columns names
df['g'] = df.groupby(['solution_id','variable']).cumcount().add(1)

#pivoting and sorting MultiIndex
df = (df.pivot('solution_id',['variable', 'g'], 'value')
         .sort_index(level=1, axis=1, sort_remaining=False))

#flatten MultiIndex
df.columns = df.columns.map(lambda x: f'{x[0]}_{x[1]}')

---

print (df)
            type_labour_1 labour_unit_1 est_labour_quantity_1  \
solution_id                                                     
10                  WorkA        Person                   2.0   
11                  WorkC        Person                   3.0   

            est_labour_costs_1 est_labour_total_costs_1 type_labour_2  \
solution_id                                                             
10                       300.0                    600.0         WorkB   
11                       300.0                    900.0         WorkD   

            labour_unit_2 est_labour_quantity_2 est_labour_costs_2  \
solution_id                                                          
10                 Person                   2.0             300.0.   
11                 Person                   2.0             300.0.   

            est_labour_total_costs_2  
solution_id                           
10                             600.0  
11                             600.0  

extracting dataframe cell value in for loop

If you want to iterate through each i,j value of the data frame, you can do something like this:

for i in range(df.shape[0]):
    for j in range(df.shape[1]):
        print(df.iloc[i, j])

Get a numerical cell value in a Pandas DataFrame

It would be this simple:

float(df['column_name'][row_number])

where:

• df is the dataframe
• column_name is the name of the column
• row_number is an integer number for the row.

the function float() does the conversion to a float.

Example dataframe:

   year  average_temperature
0  1820              -6.1100
1  1821              -5.5600
2  1822              -5.8500
3  1823              -6.3550
4  1824              -5.1754
5  1825              -5.1500

The float of the row 3 average temperature:

float(df['average_temperature'][3])

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