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Articles by Rishikesh Kumar Rishi
1,016 articles
How to iterate over rows in a DataFrame in Pandas?
To iterate rows in a DataFrame in Pandas, we can use the iterrows() method, which will iterate over DataFrame rows as (index, Series) pairs.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Iterate df using df.iterrows() method.Print each row with index.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print "Given DataFrame:", df for index, row in df.iterrows(): print "Row ", index, "contains: " print row["x"], row["y"], row["z"]OutputGiven DataFrame: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 Row 0 contains: 5 4 4 Row 1 contains: 2 1 1 Row 2 contains: 1 5 5 Row 3 contains: 9 10 0
Read MoreSelect rows from a Pandas DataFrame based on column values
To select rows from a DataFrame based on column values, we can take the following Steps −Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Use df.loc[df["x"]==2] to print the DataFrame when x==2.Similarly, print the DataFrame when (x >= 2) and (x < 2).Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print "Given DataFrame is:", df print "When column x value == 2:", df.loc[df["x"] == 2] ...
Read MoreHow to rename column names in a Pandas DataFrame?
To rename columns in a Pandas DataFrame, we can override df.columns with the new column names.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Override the columns with new list of column names.Print the DataFrame again with the renamed column names.Exampleimport 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.columns = ["a", "b", "c"] print("After renaming, DataFrame is:", df)OutputInput DataFrame is: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 After renaming, DataFrame is: a b c 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0
Read MoreSelect multiple columns in a Pandas DataFrame
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.Exampleimport 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
Read MoreHow to get the row count of a Pandas DataFrame?
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).Exampleimport 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
Read MoreHow to get the list of column headers from a Pandas DataFrame?
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.Exampleimport 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']
Read MoreHow to change the order of Pandas DataFrame columns?
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.Exampleimport 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:] + cols[:-1] ...
Read MoreCreate a Pandas Dataframe by appending one row at a time
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.Exampleimport 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 appending ...
Read MoreHow are iloc and loc different in Python Pandas?
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.Exampleimport 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:", s.iloc[0]OutputInput ...
Read MoreDeleting a DataFrame row in Python Pandas based on column value
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.Exampleimport 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 = ...
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