10 python mistakes you shouldn t make as a data analyst

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certainly! here’s a comprehensive tutorial on 10 common mistakes that data analysts should avoid when using python. each example includes a brief explanation and code snippets to illustrate the mistake.

1. *not using libraries for data manipulation*
*mistake:* writing code from scratch instead of using libraries like pandas.

*example:*
```python
bad practice: manually calculating the mean
data = [10, 20, 30, 40]
mean = sum(data) / len(data) this works, but it's not optimal.

good practice: using pandas
import pandas as pd
df = pd.dataframe(data, columns=['values'])
mean = df['values'].mean() this is clearer and more efficient.
```

2. *ignoring data types*
*mistake:* not being aware of data types can lead to incorrect analyses.

*example:*
```python
bad practice: mixing types
data = ["1", "2", "3"]
total = sum(data) this raises a typeerror.

good practice: transforming to correct type
data = list(map(int, data))
total = sum(data) now this works correctly.
```

3. *not handling missing data*
*mistake:* ignoring nan values can skew your analysis.

*example:*
```python
import pandas as pd

bad practice: ignoring nan values
data = {'a': [1, 2, none, 4]}
df = pd.dataframe(data)
mean = df['a'].mean() this returns nan.

good practice: handling nan values
mean = df['a'].mean(skipna=true) this calculates the mean correctly.
```

4. *hardcoding values*
*mistake:* hardcoding values limits flexibility and reusability.

*example:*
```python
bad practice: hardcoding
threshold = 50
filtered_data = df[df['values'] threshold]

good practice: parameterizing
def filter_data(df, threshold):
return df[df['values'] threshold]

filtered_data = filter_data(df, 50) more reusable.
```

5. *overusing loops*
*mistake:* using loops for operations that can be vectorized with numpy or pandas.

*example:*
```python
bad practice: using a loop to double values
data = [1, 2, 3, 4]
doubled = []
for num in data:
doubled.append(num ...

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