When you want to combine dataframes, you can do this by merging them on a specified key. E.g. if you're using this functionality multiple times throughout an implementation): following to @Allen response 3 Efficient Ways to Filter a Pandas DataFrame Column by Substring if you deal with a large dataset), you can specify your conditions in a list and use np.select: This gives the same results as the previous code example, but with better performance. document.getElementById("ak_js_1").setAttribute("value",(new Date()).getTime()); SparkByExamples.com is a Big Data and Spark examples community page, all examples are simple and easy to understand, and well tested in our development environment, | { One stop for all Spark Examples }, Convert Series to Dictionary(Dict) in Pandas, https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.split.html, Pandas Combine Two Columns of Text in DataFrame, Pandas Drop Level From Multi-Level Column Index, Pandas Group Rows into List Using groupby(), Export Pandas to CSV without Index & Header, Pandas Combine Two DataFrames With Examples, Pandas Create DataFrame From Dict (Dictionary), Pandas Replace NaN with Blank/Empty String, Pandas Replace NaN Values with Zero in a Column, Pandas Change Column Data Type On DataFrame, Pandas Select Rows Based on Column Values, Pandas Delete Rows Based on Column Value, Pandas How to Change Position of a Column, Pandas Append a List as a Row to DataFrame. Join is another method in pandas which is specifically used to add dataframes beside one another. It is possible to create the same columns (first- and lastname) in one line, with zip, apply and lambda: A regular way for column creation is to use a dictionary for mapping values. If you already know what a package is, you can jump to Pandas DataFrame and Series section to look at topics covered straightaway. What are the advantages of running a power tool on 240 V vs 120 V? Create a new column by assigning the output to the DataFrame with a new column name in between the []. How to create new columns derived from existing columns pandas 2.0.0 565), Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. Lets create age groups in our dataframe. You do have to convert the type on non-string columns. Generate points along line, specifying the origin of point generation in QGIS. Data Scientist with a passion for math Currently working at IKEA and BigData Republic I share tips & tricks and fun side projects, df[['firstname', 'lastname', 'bruto', 'netto', 'netto_times_2', 'tax', 'fullname']].head(), df[['birthdate', 'year_of_birth', 'age', 'days_since_birth']].head(), df['netto_ranked'] = df['netto'].rank(ascending=False), df['netto_pct_ranked'] = df['netto'].rank(pct=True), df[['netto','netto_ranked', 'netto_pct_ranked']].head(), df['child'] = np.where(df['age'] < 18, 1, 0), df['male'] = np.where(df['gender'] == 'M', 1, 0), df[['age', 'gender', 'child', 'male']].head(), # applying an existing function to a column, df['tax'] = df.apply(lambda row: row.bruto - row.netto, axis=1), # apply to dataframe, use axis=1 to apply the function to every row, df['salary_age_relation'] = df.apply(age_salary, axis=1). }, inplace=True). They are: Let us look at each of them and understand how they work. So we pass '_' as the first argument to the Series.str.split() function.