从熊猫数据框中选择特定行

Question

I just want to know if there is any function in pandas that selects specific rows based on index from a dataframe without having to write your own function.

For example: selecting rows with index [15:50] from a large dataframe.

I have written this function, but I would like to know if there is a shortcut.

def split_concat(data , first , last):
    data_out = pd.DataFrame()
    for i in range(first, last +1):
        data_split = data.loc[i]
        data_out = pd.concat([data_out,data_split],axis = 0)

    return data_out

Answer 1

You could use either pandas.DataFrame.loc or pandas.DataFrame.iloc . See examples below.

import pandas as pd

d = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
     {'a': 100, 'b': 200, 'c': 300, 'd': 400},
     {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 },
     {'a': 1500, 'b': 2500, 'c': 3500, 'd': 4500}]

df = pd.DataFrame(d)

print(df)               # Print original dataframe
print(df.loc[1:2])      # Print rows with index 1 and 2, (method 1)
print(df.iloc[1:3])     # Print rows with index 1 and 2, (method 2)

Original dataframe: print(df) will print:

      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000
3  1500  2500  3500  4500

And print(df.loc[1:2]) for index selection by label:

      a     b     c     d
1   100   200   300   400
2  1000  2000  3000  4000

And print(df.iloc[1:3]) for row selection by integer. As mentioned by ALollz, rows are treated as numbers from 0 to len(df) :

      a     b     c     d
1   100   200   300   400
2  1000  2000  3000  4000

A rule of thumb could be:

• Use .loc when you want to refer to the actual value of the index, being a string or integer.

• Use .iloc when you want to refer to the underlying row number which always ranges from 0 to len(df) .

Note that the end value of the slice in .loc is included. This is not the case for .iloc , and for Python slices in general.

Pandas in general

Pandas has 'easy' ways of doing all sorts of stuff like this. If you have a problem that you think is common for manipulation of tabular data, try searching for pandas ways of getting it done before inventing it yourself. Pandas will almost always have a syntactically concise and computationally faster way of doing things than what we can write ourselves.

Answer 2

用这个：

rowData = your_df.loc[ 'index' , : ]