Как найти значение в таблице pandas

dframe['Last Name'] == 'Turner' 

The line above produces a pandas.Series of boolean items, that represent whether or not each entry in the 'Last Name' column matches 'Turner'

You can use that pandas.Series of boolean items to index your dataframe:

dframe[dframe['Last Name'] == 'Turner']

That should leave you with your desired selection of rows.

Now, if you only wish to look at the 'First Name' for the selected rows, you can do

dframe[dframe['Last Name'] == 'Turner']['First Name']

If you want to do a compound search for both first name and last name, you need to perform a bitwise boolean operation between results of individual searches:

dframe[(dframe['First Name'] == 'John') & (dframe['Last Name'] == 'Turner')]

Finally, to give you a little bonus, if you wish to find all last names that contain 'Turner', say something like 'Turner-Jones', you can do the following:

dframe[dframe['Last Name'].str.contains('Turner')] 

In the line above you are using the .str accessor on the pandas.Series, which gives you access to a set of very convenient string methods. You can read more about it in the documentation.

Below I show a working example from an IPython session:

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: first_names = ['John', 'Tom', 'Fred', 'Michael', 'Andrew']

In [4]: last_names = ['Turner', 'Harden', 'Bryant', 'Davis', 'Turner']

In [5]: df = pd.DataFrame(list(zip(first_names, last_names)), columns=['First Na
me', 'Last Name'])

In [6]: df
Out[6]:
  First Name Last Name
0       John    Turner
1        Tom    Harden
2       Fred    Bryant
3    Michael     Davis
4     Andrew    Turner

In [7]: df[df['Last Name'] == 'Turner']
Out[7]:
  First Name Last Name
0       John    Turner
4     Andrew    Turner

In [8]: df[(df['First Name'] == 'John') & (df['Last Name'] == 'Turner')]
Out[8]:
  First Name Last Name
0       John    Turner

In [9]: df[df['Last Name'].str.contains('r')]
Out[9]:
  First Name Last Name
0       John    Turner
1        Tom    Harden
2       Fred    Bryant
4     Andrew    Turner

In [10]: (df['Last Name'] == 'Turner').any()
Out[10]: True

Notice that in the input labeled In[10] I went ahead and verified if there were any matches by calling the any() method on the boolean pandas.Series. This can be a helpful way of debugging your search if you are having issues getting the results you expect.

Pandas

Python Pandas Code Example to Search for a Value in a DataFrame Column

When working with a large dataset on any machine learning or data science project, there is a need to search for some values in a feature, and for that values, we need to get the values from other features.  Searching for values within a dataset might sound complicated but Python Pandas makes it easy.

The Python Pandas Code below does the following:
1. Creates data dictionary and converts it into DataFrame
2. Uses the “where” function to filter out desired data columns. The pandas.DataFrame.where() function is like the if-then idiom which checks for a condition to return the result accordingly.

Python Pandas Sample Code to Find Value in DataFrame 

Below is the pandas code in python to search for a value within a Pandas DataFrame column –

Step 1 – Import the library

import pandas as pd

We have only imported the python pandas library which is needed for this code example.

Step 2 – Setting up the Data

We have created a dictionary of data and passed it to pd.DataFrame to make a dataframe with columns ‘first_name’, ‘last_name’, ‘age’, ‘Comedy_Score’ and ‘Rating_Score’.


raw_data = {'first_name': ['Sheldon', 'Raj', 'Leonard', 'Howard', 'Amy'],
'last_name': ['Copper', 'Koothrappali', 'Hofstadter', 'Wolowitz', 'Fowler'],
'age': [42, 38, 36, 41, 35],
'Comedy_Score': [9, 7, 8, 8, 5],
'Rating_Score': [25, 25, 49, 62, 70]}

df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age',
'Comedy_Score', 'Rating_Score'])
print(df)

 Try A Few More Pandas Code Examples With These Python Pandas Projects with Source Code

Step 3 – Searching the Values in the DataFrame

We are searching the data in the feature Rating_Score which have values less than 50 and for those values, we are selecting the corresponding values in comedy_Score.


print(df['Comedy_Score'].where(df['Rating_Score'] < 50))

The output is as shown below –

  first_name     last_name  age  Comedy_Score  Rating_Score
0    Sheldon        Copper   42             9            25
1        Raj  Koothrappali   38             7            25
2    Leonard    Hofstadter   36             8            49
3     Howard      Wolowitz   41             8            62
4        Amy        Fowler   35             5            70

0    9.0
1    7.0
2    8.0
3    NaN
4    NaN
Name: Comedy_Score, dtype: float64