How to update rows and columns using Python Pandas

1. Create a Pandas data set

In order to create a data frame, pandas provides the function name pd.DataFrame, which can help you create a data frame from some data. Let's see how it works.

#创建一个字典

import pandas as pd

fruit_data = {"Fruit": ['Apple','Avacado','Banana','Strawberry','Grape'],"Color": ['Red','Green','Yellow','Pink','Green'],
"Price": [45, 90, 60, 37, 49]
}

fruit_data

Here, we create a Python dictionary that includes some data items. Now, we are asked to turn this dictionary into a Pandas dataset.

#Dataframe 

data = pd.DataFrame(fruit_data)
data

That’s perfect! Using pandas’ pd.DataFrame function, we can easily convert a dictionary into a pandas dataset. Our dataset is now ready for future operations.

Update Columns

Sometimes the columns or names of features are inconsistent. It can be uppercase and lowercase of the alphabet, etc. Having a unified design helps us use these features effectively.

So as a first step we will understand how to update/change column or feature names in the data.

#update the column name

data.rename(columns = {'Fruit':'Fruit Name'})

#Simple as shown above. You can even update multiple column names at once. To do this we have to add additional column names separated by commas under curly braces.

#multile column update

data.rename(columns = {'Fruit':'Fruit Name','Colour':'Color','Price':'Cost'})

Like this, we can update all columns at the same time.

Update case of column names

When processing a data set with many columns, we may encounter inconsistent column names.

In our data, you can observe that the first letter of all column names is capitalized. It is always recommended to use common case for all column names.

Well, we can convert them to uppercase or lowercase.

#lower case

data.columns.str.lower()
data

Now, all of our column names are lowercase.

Updating rows

Like updating columns, updating rows is also very simple. We must find the row value before we can update the row with the new value.

We can use pandas loc function to locate rows.

#updating rows

data.loc[3]

Fruit    Strawberry
Color          Pink
Price            37
Name: 3, dtype: object

We found row 3 which contains the details of the fruit strawberry. We need to update this row to provide a new fruit name as Pineapple and its details.

#update

data.loc[3] = ['PineApple','Yellow','48']
data

I hope you all also find it easy to update the value of a row in your data. Now, let's say we only need to update some details in the row, rather than the entire detail. So, what do you think about this?

#更新特定值

data.loc[3, ['Price']]

Price    48
Name: 3, dtype: object

We only need to update the price of the fruit located in row 3. We know that the current price of the fruit is 48. However, we have to update it to 65. Let's do this.

#updating 

data.loc[3, ['Price']] = [65]
data

We update the price of the fruit pineapple to 65 with just one line of python code. This is how it works. simple.

Update rows and columns based on conditions

Yes, we will now update row values ​​based on specific conditions. Finally, we want some meaningful values ​​that should help our analysis.

Let's define our conditions.

#Condition

updated = data['Price'] > 60
updated

What we want to do here is update the price of fruits above 60 to expensive.

0    False
1     True
2    False
3     True
4    False
Name: Price, dtype: bool

According to the output, we have 2 fruits with price above 60. Let's list these fruits as expensive in the data.

#Updating

data.loc[updated, 'Price'] = 'Expensive'
data

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