To sort multiple columns of a Pandas DataFrame, we can use the sort_values() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable col to sort the column.Print the sorted DataFrame.Example Live Demoimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 7, 0],       "y": [4, 7, 5, 1],       "z": [9, 3, 5, 1]    } ) print "Input DataFrame is:", df col = ["x", "y"] df = df.sort_values(col, ascending=[False, True]) print "After sorting column ", col, "DataFrame is:", dfOutputInput DataFrame is:    x  y  z 0  5  4  9 1  2  7  3 2  7  5  5 3  0  1  1 After sorting column ['x', 'y'] DataFrame is:    x  y  z 2  7  5  5 0  5  4  9 1  2  7  3 3  0  1  1

To sort a column in a Pandas DataFrame, we can use the sort_values() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print input DataFrame, df.Initialize a variable col to sort the column.Print the sorted DataFrame.Example Live Demoimport pandas as pd df = pd.DataFrame(    {      "x": [5, 2, 7, 0],      "y": [4, 10, 5, 1],     `"z": [9, 3, 5, 1]   } ) print "Input DataFrame is:", df col = "x" df = df[col].sort_values(ascending=False) print "After sorting column ", col, "DataFrame is:", dfOutputInput DataFrame is:    x  y  z 0  5  4  9 1  2 10  3 2  7  5  5 3  0  1  1 After sorting column x DataFrame is: 2  7 0  5 1  2 3  0 Name: x, dtype: int64

We can use apply() function on a column of a DataFrame with lambda expression.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print input DataFrame, df.Override column x with lambda x: x*2 expression using apply() method.Print the modified DataFrame.Example Live Demoimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 1, 5],       "y": [4, 10, 5, 10],       "z": [1, 1, 5, 1]    } ) print "Input DataFrame is:", df df['x'] = df['x'].apply(lambda x: x * 2) print "After applying multiplication of 2 DataFrame is:", dfOutputInput DataFrame is:    x  y  z 0  5  4  1 1  2 10  1 2  1  5  5 3  5 10  1 After applying multiplication of 2 DataFrame is:     x  y   z 0  10  4   1 1   4 10   1 2   2  5   5 3  10 10   1

To count the frequency of a value in a DataFrame column in Pandas, we can use df.groupby(column name).size() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Print frequency of column, x.Print frequency of column, y.Print frequency of column, z.Example Live Demoimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 1, 5],       "y": [4, 10, 5, 10],       "z": [1, 1, 5, 1]    } ) print "Input DataFrame is:", df col = "x" count = df.groupby('x').size() print "Frequency of values in column ", col, ... Read More

To check if a column exists in a Pandas DataFrame, we can take the following Steps −StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a col variable with column name.Create a user-defined function check() to check if a column exists in the DataFrame.Call check() method with valid column name.Call check() method with invalid column name.Example Live Demoimport pandas as pd def check(col):    if col in df:       print "Column", col, "exists in the DataFrame."    else:       print "Column", col, "does not exist in the DataFrame." df = pd.DataFrame( ... Read More

To select all columns except one column in Pandas DataFrame, we can use df.loc[:, df.columns != ].StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable col with column name that you want to exclude.Use df.loc[:, df.columns != col] to create another DataFrame excluding a particular column.Print the DataFrame without col column.Example Live Demoimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 1, 9],       "y": [4, 1, 5, 10],       "z": [4, 1, 5, 0]    } ) print("Input DataFrame is:", df) col ... Read More

To get a value from the cell of a DataFrame, we can use the index and col variables.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize the index variable.Initialize the col variable.Get the cell value corresponding to index and col variable.Print the cell value.Example Live Demoimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 1, 9],       "y": [4, 1, 5, 10],       "z": [4, 1, 5, 0]    } ) print("Input DataFrame is:", df) index = 2 col = "y" cell_val = df.iloc[index][col] print ... Read More

To replace NaN values by zeroes or other values in a column of a Pandas DataFrame, we can use df.fillna() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Use df.fillna(0) to replace NaN in DataFrame with value 0.Similarly use df.fillna(5) and df.fillna(7) to replace NaN in DataFrame with 5 and 7, respectively.Print the replaced NaN, DataFrame.Example Live Demoimport pandas as pd import numpy as np df = pd.DataFrame(    {       "x": [5, np.nan, 1, np.nan],       "y": [np.nan, 1, np.nan, 10],       "z": [np.nan, 1, np.nan, np.nan]    } ... Read More

To replace NaN values by zeroes or other values in a column of Pandas Series, we can use s.fillna() method.StepsCreate a one-dimensional ndarray with axis labels (including time series).Print the input series.Use s.fillna(0) to replace NaN in the series with value 0.Similarly, use s.fillna(5) and s.fillna(7) to replace NaN in series with values 5 and 7, respectively.Print the replaced NaN series.Example Live Demoimport pandas as pd import numpy as np s = pd.Series([1, np.nan, 3, np.nan, 3, np.nan, 7, np.nan, 3]) print "Input series is:", s print "After replacing NaN with 0:", s.fillna(0) print "After replacing NaN with 5:", s.fillna(5) ... Read More

To create a DataFrame with some index, we can pass a list of values and assign them into index in DataFrame Class.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Put a list of indices in the index of DataFrame class.Print the DataFrame with the customized index.Example Live Demoimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 1, 9],       "y": [4, 1, 5, 10],       "z": [4, 1, 5, 0]    } ) print "Input DataFrame is:", df df = pd.DataFrame(    {       "x": [5, 2, 1, 9],       "y": [4, 1, 5, 10],       "z": [4, 1, 5, 0]    },    index=["John", "Jacob", "Ally", "Simon"] ) print "With Customized Index: ", dfOutputInput DataFrame is:    x  y  z 0  5  4  4 1  2  1  1 2  1  5  5 3  9  10 0 With Customized Index:        x  y   z John   5  4   4 Jacob  2  1   1 Ally   1  5   5 Simon  9  10  0