To create new column based on values from other columns or apply a function of multiple columns, row-wise with Python Pandas, we can use the apply method.
For instance, we write
df['col_3'] = df.apply(lambda x: f(x.col_1, x.col_2), axis=1)
to call apply on data frame df with a lambda function that calls function f on the col_1 and col_2 column values.
And then we assign the returned values to column col_3.
To apply a function to two columns of Python Pandas dataframe, we can use the apply method.
For instance, we write
df['col_3'] = df.apply(lambda x: f(x.col_1, x.col_2), axis=1)
to call apply on data frame df with a lambda function that calls function f on the col_1 and col_2 column values.
And then we assign the returned values to column col_3.
To convert Python dict into a Pandas dataframe, we can call DataFrame with the dictuionary.
For instance, we write
dict_ = {'key 1': 'value 1', 'key 2': 'value 2', 'key 3': 'value 3'}
pd.DataFrame([dict_])
to call DataFrame with the dict_ dictionary in a list to create the data frame.
To delete DataFrame row in Pandas based on column value, we put the condition of the items to return in the brackets.
For instance, we write
df = df[df.line_race != 0]
to return the df data frame values where the line_race column value isn’t 0 with
df[df.line_race != 0]
To apply multiple functions to multiple groupby columns with Python Pandas, we can use the groupby and agg methods.
For instance, we write
df.groupby('group').agg(
a_sum=('a', 'sum'),
a_mean=('a', 'mean'),
b_mean=('b', 'mean'),
c_sum=('c', 'sum'),
d_range=('d', lambda x: x.max() - x.min())
)
to call agg on the groups returned by groupby with some arguments to computed aggregate values for various columns.
We compute the sum of columns in a, the mean of a and b, the sum of c and the differnce between the max and min columns in d with agg.