Create New Column Based On String
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pandas create column based on values in other columns
create new column in dataframe based on other columns python
create a new column based on existing column in pandas
pandas str contains list
make new dataframe column
I have a data frame, want to create a column based on the string in column1_sport.
import pandas as pd df = pd.read_csv('C:/Users/test/dataframe.csv', encoding = 'iso-8859-1')
column1_sport baseball basketball tennis boxing golf
I want to look for certain strings ("ball" or "box") and create a new column based on whether the column contains that word. If the dataframe doesn't contain that word, add "other". See below.
column1_sport column2_type baseball ball basketball ball tennis other boxing box golf other
For multiple conditions I suggest
np.select. For example:
values = ['ball', 'box'] conditions = list(map(df['column1_sport'].str.contains, values)) df['column2_type'] = np.select(conditions, values, 'other') print(df) # column1_sport column2_type # 0 baseball ball # 1 basketball ball # 2 tennis other # 3 boxing box # 4 golf other
Pandas: Create new column based on first x letters of string in other , I want to create a new column based on the first 3 letter of string contained in existing column. Example: Initial dataframe: col1. XYAZSZ. CXJSHD. New dataframe: Column Expression node is the one where you can combine columns and strings with conditions. You can try that or other option is to use two nodes. First use String Manipulation node to get new column and after that Rule engine node where you will take value from new column or put “Linear”.
df["column2_type"] = df.column1_sport.apply(lambda x: "ball" if "ball" in x else ("box" if "box" in x else "Other")) df column1_sport column2_type 0 baseball ball 1 basketball ball 2 tennis Other 3 boxing box 4 golf Other
Incase you have more complex conditions
def func(a): if "ball" in a.lower(): return "ball" elif "box" in a.lower(): return "box" else: return "Other" df["column2_type"] = df.column1_sport.apply(lambda x: func(x))
Create a new column in Pandas DataFrame based on the existing , Split a String into columns using regex in pandas DataFrame · Join two text columns into a single column in Pandas · Python | Delete rows/columns from Create a new column in Pandas DataFrame based on the existing columns While working with data in Pandas, we perform a vast array of operations on the data to get the data in the desired form. One of these operations could be that we want to create new columns in the DataFrame based on the result of some operations on the existing columns in the
You can use a nested np.where
cond1 = df.column1_sport.str.contains('ball') cond2 = df.column1_sport.str.contains('box') df['column2_type'] = np.where(cond1, 'ball', np.where(cond2, 'box', 'other') ) column1_sport column2_type 0 baseball ball 1 basketball ball 2 tennis other 3 boxing box 4 golf other
Python, Pandas provide a method to split string around a passed separator/delimiter. Return Type: Series of list or Data frame depending on expand Parameter The Data frame is then used to create new columns and the old Name column is Add a new column in DataFrame with values based on other columns. Let’s add a new column ‘Percentage’ where entry at each index will be calculated by the values in other columns at that index i.e. dfObj['Percentage'] = (dfObj['Marks'] / dfObj['Total'] ) * 100
How to create a new column based on the values from multiple , Just needed == replacing with = and the braces adding. $NF } 1' file col1 col2 col3 col4 col5 newcol 1 3 4 string string 0 4 2 1 string string 4. When the awk script has added and outputted the new column header, it starts to compute the new Hi, I have a table with a column with date values in the following format: eg. 20140119 I want to create/ add a new column which will have the following format: 2014-01-19 I tried adding a custom column, but not sure about the function to be used to get a substring.
create a new column based on existing columns using if else , Make new columns from existing data and build custom functions. You can do this by creating a derived column based on the values in the platform column. This new column is You can define mobile platforms in this list of strings: Input. Create a calculate column using DAX as below: lBStatus_ = VAR Most_Current_Year_Sem = MAX(Sheet1[Year Sem]) RETURN CALCULATE(VALUES(Sheet1[IBStatus]), FILTER(ALLEXCEPT(Sheet1, Sheet1[StudentID]), Sheet1[Year Sem] = Most_Current_Year_Sem))
Deriving New Columns & Defining Python Functions, Solved: I am trying to fill a new column with String values based on an example of how you'd like to input that, I'm sure we can make it work. Actually we don’t have to rely on NumPy to create new column using condition on another column. Instead we can use Panda’s apply function with lambda function. gapminder['gdpPercap_ind'] = gapminder.gdpPercap.apply(lambda x: 1 if x >= 1000 else 0) gapminder.head()
- @nia4life, go with jpp's np.select for more conditions