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Convert a column to row name/index in Pandas
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Get all rows in a Pandas DataFrame containing given substring

Last Updated : 24 Dec, 2018
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Let's see how to get all rows in a Pandas DataFrame containing given substring with the help of different examples. Code #1: Check the values PG in column Position Python3 1== 
# importing pandas  import pandas as pd  # Creating the dataframe with dict of lists df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'],                    'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'],                    'Position': ['PG', 'PG', 'UG', 'PG', 'UG'],                    'Number': [3, 4, 7, 11, 5],                    'Age': [33, 25, 34, 35, 28],                    'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'],                    'Weight': [89, 79, 113, 78, 84],                    'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'],                    'Salary': [99999, 99994, 89999, 78889, 87779]},                    index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5']) print(df, "\n")  print("Check PG values in Position column:\n") df1 = df['Position'].str.contains("PG") print(df1)
Output: But this result doesn't seem very helpful, as it returns the bool values with the index. Let's see if we can do something better.   Code #2: Getting the rows satisfying condition Python3 1== 
# importing pandas as pd import pandas as pd  # Creating the dataframe with dict of lists df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'],                    'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'],                    'Position': ['PG', 'PG', 'UG', 'PG', 'UG'],                    'Number': [3, 4, 7, 11, 5],                    'Age': [33, 25, 34, 35, 28],                    'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'],                    'Weight': [89, 79, 113, 78, 84],                    'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'],                    'Salary': [99999, 99994, 89999, 78889, 87779]},                    index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5'])  df1 = df[df['Position'].str.contains("PG")] print(df1)
Output:   Code #3: Filter all rows where either Team contains 'Boston' or College contains 'MIT'. Python3 1== 
# importing pandas import pandas as pd  # Creating the dataframe with dict of lists df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'],                    'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'],                    'Position': ['PG', 'PG', 'UG', 'PG', 'UG'],                    'Number': [3, 4, 7, 11, 5],                    'Age': [33, 25, 34, 35, 28],                    'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'],                    'Weight': [89, 79, 113, 78, 84],                    'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'],                    'Salary': [99999, 99994, 89999, 78889, 87779]},                    index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5'])   df1 = df[df['Team'].str.contains("Boston") | df['College'].str.contains('MIT')] print(df1)
Output:   Code #4: Filter rows checking Team name contains 'Boston and Position must be PG. Python3 1== 
# importing pandas module  import pandas as pd     # making data frame  df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")    df1 = df[df['Team'].str.contains('Boston') & df['Position'].str.contains('PG')] df1
Output:   Code #5: Filter rows checking Position contains PG and College must contains like UC. Python3 1== 
# importing pandas module  import pandas as pd     # making data frame  df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")    df1 = df[df['Position'].str.contains("PG") & df['College'].str.contains('UC')] df1
Output:  

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Convert a column to row name/index in Pandas

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      Let's create a program in python which will replace the white spaces in a string with the character that occurs in the string very least using the Pandas library.  Example 1:  String S = "akash loves gfg" here: 'g' comes: 2 times 's' comes: 2 times 'a' comes: 2 times 'h' comes: 1 time  'o' comes: 1
      2 min read
    • How to Convert Floats to Strings in Pandas DataFrame?
      In this post, we'll see different ways to Convert Floats to Strings in Pandas Dataframe? Pandas Dataframe provides the freedom to change the data type of column values. We can change them from Integers to Float type, Integer to String, String to Integer, Float to String, etc. There are three methods
      4 min read

    Accessing and Manipulating Data in DataFrame

    • Access Index of Last Element in pandas DataFrame in Python
      In this article, we are going to see how to access an index of the last element in the pandas Dataframe. To achieve this, we can use Dataframe.iloc, Dataframe.iget, and Dataframe.index. let's go through all of them one by one.   Dataframe.iloc -  Pandas Dataframe.iloc is used to retrieve data by spe
      3 min read
    • Replace Characters in Strings in Pandas DataFrame
      In this article, we are going to see how to replace characters in strings in pandas dataframe using Python.  We can replace characters using str.replace() method is basically replacing an existing string or character in a string with a new one. we can replace characters in strings is for the entire
      3 min read
    • Replace values of a DataFrame with the value of another DataFrame in Pandas
      In this article, we will learn how we can replace values of a DataFrame with the value of another DataFrame using pandas.  It can be done using the DataFrame.replace() method. It is used to replace a regex, string,  list, series, number, dictionary, etc. from a DataFrame, Values of the DataFrame met
      5 min read
    • Replace negative values with latest preceding positive value in Pandas DataFrame
      In this article, we will discuss how to replace the negative value in Pandas DataFrame Column with the latest preceding positive value. While doing this there may arise two situations -  Value remains unmodified if no proceeding positive value existsValue update to 0 if no proceeding positive value
      3 min read
    • How to add column from another DataFrame in Pandas ?
      In this discussion, we will explore the process of adding a column from another data frame in Pandas. Pandas is a powerful data manipulation library for Python, offering versatile tools for handling and analyzing structured data. Add column from another DataFrame in Pandas There are various ways to
      6 min read

    DataFrame Visualization and Exporting

    • How to render Pandas DataFrame as HTML Table?
      Pandas in Python can convert a Pandas DataFrame to a table in an HTML web page. The pandas.DataFrame.to_html() method is used to render a Pandas DataFrame into an HTML format, allowing for easy display of data in web applications. In this article, we will understand how to use the Styler Object and
      6 min read
    • Exporting Pandas DataFrame to JSON File
      Pandas a powerful Python library for data manipulation provides the to_json() function to convert a DataFrame into a JSON file and the read_json() function to read a JSON file into a DataFrame. In this article we will explore how to export a Pandas DataFrame to a JSON file with detailed explanations
      2 min read
    • Create and display a one-dimensional array-like object using Pandas in Python
      Series() is a function present in the Pandas library that creates a one-dimensional array and can hold any type of objects or data in it. In this article, let us learn the syntax, create and display one-dimensional array-like object containing an array of data using Pandas library. pandas.Series() S
      2 min read
    • Export Pandas dataframe to a CSV file
      When working on a Data Science project one of the key tasks is data management which includes data collection, cleaning and storage. Once our data is cleaned and processed it’s essential to save it in a structured format for further analysis or sharing. A CSV (Comma-Separated Values) file is a widel
      3 min read
    • Display the Pandas DataFrame in Heatmap style
      Pandas library in the Python programming language is widely used for its ability to create various kinds of data structures and it also offers many operations to be performed on numeric and time-series data. By displaying a panda dataframe in Heatmap style, the user gets a visualisation of the numer
      6 min read
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