# read\_excel()

**pip install openpyxl**

<pre class="language-python"><code class="lang-python"><strong>import pandas as pd
</strong># Load data from an Excel file
df = pd.read_excel('ranking.xlsx', sheet_name='Sheet1')
print (df.to_string())
</code></pre>

**Output:**

```python
    Pos                Team T  Matches M  Points P  Rating R
0     1             India IND         69     18445       267
1     2           England ENG         52     13555       261
2     3          Pakistan PAK         58     14775       255
3     4       South Africa SA         44     11170       254
4     5        New Zealand NZ         53     13371       252
5     6         Australia AUS         47     11784       251
6     7        West Indies WI         54     12855       238
7     8          Sri Lanka SL         53     12485       236
8     9        Bangladesh BAN         55     12506       227
9    10       Afghanistan AFG         36      7928       220
10   11          Zimbabwe ZIM         49      9597       196
11   12           Ireland IRE         58     10995       190
12   13               UAE UAE         32      5898       184
13   14           Namibia NAM         32      5846       183
14   15          Scotland SCO         24      4373       182
15   16             Nepal NEP         30      5387       180
16   17       Netherlands NED         32      5668       177
17   18              Oman OMA         26      4090       157
18   19  Papua New Guinea PNG         24      3495       146
19   20            Canada CAN         24      3297       137
20   21          Hong Kong HK         24      3207       134
21   22            Jersey JSY         23      2924       127
22   23             Qatar QAT         18      2227       124
23   24            Uganda UGA         46      5648       123
24   25     United States USA         16      1908       119
25   26            Kuwait KUW         22      2504       114
26   27          Malaysia MAS         38      4318       114
27   28             Kenya KEN         31      3272       106
28   29           Bahrain BRN         33      3436       104
29   30         Singapore SIN         28      2857       102
30   31          Tanzania TAN         36      3598       100
31   32             Italy ITA         19      1818        96
32   33      Saudi Arabia SDA         13      1234        95
33   34           Bermuda BRM         18      1671        93
34   35           Germany GER         34      2996        88
35   36             Spain ESP         26      2267        87
36   37           Denmark DEN         21      1518        72
37   38          Guernsey GSY         17      1194        70
38   39    Cayman Islands CAY         12       814        68
39   40           Belgium BEL         20      1349        67
40   41           Nigeria NGR         29      1954        67
41   42           Austria AUT         26      1686        65
42   43           Vanuatu VAN         14       877        63
43   44          Portugal POR         11       644        59
44   45           Romania ROM         24      1359        57
45   46       Isle of Man IOM         15       847        56
46   47          Botswana BOT         18       992        55
47   48            Norway NOR         17       897        53
48   49           Finland FIN         17       891        52
49   50            France FRA          9       470        52
```
