Programming Articles - Page 1106 of 3363

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

To check if any value is NaN in a Pandas DataFrame, we can use isnull().values.any() method.StepsMake a series, s, one-dimensional ndarray with axis labels (including time series).Print the series, s.Check whether NaN is present or not.Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Check whether NaN is present or not.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 present = s.isnull().values.any() print "NAN is present in series: ", present df = pd.DataFrame(    {       "x": [5, ... Read More

To reset hierarchical index in Pandas, we can use reset_index() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Use groupby to get different levels of a hierarchical index and count it.Print multi-hierarchical index DataFrame.Reset the multi-hierarchical index DataFrame, using df.reset_index().Print the new updated DataFrame.Example Live Demoimport pandas as pd df = pd.DataFrame({"x": [5, 2, 1, 9], "y": [4, 1, 5, 10]}) print "Input DataFrame is:", df df1 = df.groupby(["x", "y"]).count() print "Hierarchical Index of input DataFrame is:", df1 df2 = df1.reset_index() print "After resetting: ", df2OutputInput DataFrame is:    x  y 0  5   ... Read More

Suppose, we are playing a game where we are trapped in a maze. We have to find our way out of the maze. The maze can be presented as an x m matrix, where n is the number of rows and m is the number of columns. Each cell/element of the matrix contains any of the symbols 'O', 'D', 'S', or '-'. 'O' means that the path is blocked, 'D' is the way out from the maze, 'S' is our starting position, and '-' means we can move through the path. We can move freely through any of the '-' ... Read More

Suppose, we have a chessboard and a special knight piece K, that moves in an L shape within the board. If the piece is in position (x1, y1) and if it moves to (x2, y2) the movement can be described as x2 = x1 ± a ; y2 = y1 ± b or x2 = x1 ± b ; y2 = y1 ± a ; where a and b are integer numbers. We have to find out the minimum number of moves for that chess piece to reach to reach position (n-1, n-1) on the chessboard from the position (0, ... Read More

To make a multi-index in Pandas, we can use groupby with list of columns.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the index of DataFrame count.Use groupby to get different levels of a hierarchical index and count it.Print the mulitindex set in step 4.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 print "Default index: ", df.count().index df1 = df.groupby(["x", "y"]).count() ... Read More

To convert a Pandas DataFrame to a NumPy array, we can use to_numpy().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the NumPy array of the given array, using df.to_numpy().Print the NumPy array of the given array for a specific column, using df['x'].to_numpy().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 print "DataFrame to numpy is:", df.to_numpy() print "DataFrame to numpy is:", df['x'].to_numpy()OutputInput ... Read More