Data analysis Essays (Python and Pandas and Matplotlib view data)

Notes:

Import Pandas as PD

For CSV data files, open with Pd.read_csv (), such as Train_data=pd.read_csv (")

Use Train_data.head () to view part of the data

Train_describe () can get statistics number, get average, variance and other characteristics (of course, for the numeric type of data)

For non-numeric types of data (character data), you can use train_data[' here to fill in the statistics of the label '].value_counts () statistical classification number

The results shown below correspond to the following: A label is Property_area, the label has semiurban urban rural, etc. three categories, statistics corresponding number

Import Matplot.pyplot as Plt

train_data[' label '].hist (BINS=50)

Plt.show ()

You can display the data distribution under the label, 50 for the y-axis interval, for the histogram display, the horizontal axis for the range of values, and the y-axis for quantity

Train_data.boxplot (column= ' label ')

Plt.show ()

You can display the numerical distribution under the label to see if the distribution is balanced

For example, the data distribution is not uniform, there are extreme values appear

Df.boxplot (column= ' label 1 ', by = ' Label 2 ')

Plt.show ()

The data under label 1 can then be plotted in a numerical distribution according to label 2

As indicated below, it has been classified according to the level of education, high-level wage extremes, and other conclusions can be obtained

Note: When you want to paint, the individual input drawing instructions can not display graphics, then you need to enter Plt.show () on another line, condition: import Matplotlib.pyplot as Plt

Data analysis Essays (Python and Pandas and Matplotlib view data)