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# Inspecting DataFrames
Pandas provides some simple methods to look at your dataframes:
* `[your_dataframe_name].head(5)` will provide the first 5 rows
* `[your_dataframe_name].tail(10)` will provide the last 10 rows
* `[your_dataframe_name].describe()` is a quick way to get summary statistics on a per-column basis
You can find more useful pandas functions \[[here](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html)]
```
ms.head() #this will give the first 5 rows by default. You can add any number in the () to get that number of rows
```
```
ms.tail(10) #and the last 10 rows
```
```
ms.describe() #this is a quick way to get summary statistics on a per-column basis
```
```
#What do you notice about the number of columns returned by describe vs that in the entire dataframe...
ms.shape
```
(216, 183)
```
ms.columns
```
```
missing = []
des_cols = ms.describe().columns
for col in ms.columns:
if col in des_cols:
print('found: '+ col)
else:
missing.append(col)
```
```
missing
```
```
pd.set_option('display.max_rows', 50) #This will set the number of rows you can "see" in the jupyter notebook when you inspect a dataframe
pd.set_option('display.max_columns', 200) #This will set the number of columns you can "see" in the jupyter notebook when you inspect a dataframe
```
```
ms.describe() #notice the difference in the number of columns you can see
```
---
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