Programming Articles - Page 1107 of 3363

To count the NaN values in a column in a Pandas DataFrame, we can use the isna() method with sum.StepsCreate a series, s, one-dimensional ndarray with axis labels (including time series).Print the series, s.Count the number of NaN present in the series.Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Find NaN count column wise.Print the count DataFrame.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 count = s.isna().sum() print "NAN count in series: ", count df = pd.DataFrame(   ... Read More

To delete a DataFrame row in Pandas based on column value, we can take the following Steps −StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Here, we will delete the row from the DataFrame that contains 0 in its Z-column, using df=df[df.z != 0]Print the updated DataFrame, after deleting row based on column 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 df ... Read More

Let's take an example to understand the difference between iloc and loc. Basically loc[0] returns the value present at 0 index, whereas iloc[0] returns the value present at the first location of a series.StepsCreate a one-dimensional ndarray with axis labels (including time series).Print the input series.Use loc[0] to print the value present at 0th index.Use iloc[0] to print the value present at the first location of the series table.Example Live Demoimport pandas as pd s = pd.Series(list("AEIOU"), index=[2, 1, 0, 5, 8]) print "Input series is:", s print "Value at index=0:", s.loc[0] print "Value at the 1st location of the series:", ... Read More

To write a Pandas DataFrame to CSV file, we can take the following Steps −StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Use df.to_csv to save the values of the DataFrame to a CSV (comma-separated values) file.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.to_csv("test.csv", sep='\t')OutputInput DataFrame is:    x   y  z 0  5  4  4 1  2  1  1 2  1  5  5 3  9 10  0It will create a new file ("test.csv") and save the values of the DataFrame in it.

To select the rows from a Pandas DataFrame based on input values, we can use the isin() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Create a list of values for selection of rows.Print the selected rows with the given values.Next, print the rows that were not selected.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:", df values = [1, 2] print "Selected Rows:", ... Read More

To create a Pandas DataFrame by appending one row at a time, we can iterate in a range and add multiple columns data in it.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Iterate in a range of 10.Assign values at different index with numbers.Print the created DataFrame.Example Live Demoimport pandas as pd import random df = pd.DataFrame(    {       "x": [],       "y": [],       "z": []    } ) print "Input DataFrame:", df for i in range(10):    df.loc[i] = [i, random.randint(1, 10), random.randint(1, 10)] print "After ... Read More

To change the order of DataFrame columns, we can take the following Steps −StepsMake two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Get the list of DataFrame columns, using df.columns.tolist()Change the order of DataFrame columns.Modify the order of columns of the DataFrame.Print the DataFrame after changing the columns order.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 cols = df.columns.tolist() cols = cols[-1:] + ... Read More

To get a list of Pandas DataFrame column headers, we can use df.columns.values.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the list of df.columns.values output.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 "List of headers are: ", list(df.columns.values)OutputInput DataFrame is:    x  y  z 0  5  4  4 1  2  1  1 2  1  5  5 3  9 10  0 List of headers are: ['x', 'y', 'z']

To get the row count of a Pandas DataFrame, we can use the length of DataFrame index.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the length of the DataFrame index list, len(df.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 print "Row count of DataFrame is: ", len(df.index)OutputInput DataFrame is:    x  y  z 0  5  4  4 1  2  1  1 2  1  5  5 3  9 10  0 Row count of DataFrame is: 4

To select multiple columns in a Pandas DataFrame, we can create new a DataFrame from the existing DataFrameStepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Create a new DataFrame, df1, with selection of multiple columns.Print the new DataFrame with multiple selected columns.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 df1 = df[['x', 'y']] print "After selecting multiple columns:", df1OutputInput DataFrame is:    x  y  z 0  5  4  4 1  2  1  1 2  1  5  5 3  9 10  0 After selecting multiple columns:    x  y 0  5  4 1  2  1 2  1  5 3  9 10