Shulou(Shulou.com)06/03 Report--

This article mainly explains "Pandas how to use stack and pivot to achieve data perspective", the content of the article is simple and clear, easy to learn and understand, the following please follow the editor's ideas slowly in depth, together to study and learn "Pandas how to use stack and pivot to achieve data perspective" bar!

Catalogue

Preface

First, multi-dimensional index data are obtained through statistics.

Second, use unstack to realize two-dimensional perspective of data.

Use pivot to simplify perspective

IV. The grammar of stack, unstack and pivot

1.stack

2.unstack

3.pivot

Preface

Recently, the author is studying Pandas data analysis and turning his study notes into a series of articles. This section mainly records the use of stack and pivot for data perspective in Pandas.

First, multi-dimensional index data are obtained through statistics.

For statistical scenarios of unusual scenarios, specify multiple dimensions and calculate the aggregated metrics

Example: statistics to get the "movie rating data set", each month of each score is scored how many times: (month, score 1-5, times)

Import pandas as pdimport numpy as np%matplotlib inlinedf=pd.read_csv (". / datas/ml-1m/ratings.dat", sep= "::", engine='python', names='UserID::MovieID::Rating::Timestamp'.split ("::"), header=None) df.head () # converts the timestamp to a specific time df ['padate'] = pd.to_datetime (df ["Timestamp"] Unit='s') df.head () df.dtypes# implements data statistics # for data in this format I want to see the trend of the number of times by month, different ratings, there is no way to achieve, need to convert the data into each score is a column can be achieved. Df_group=df.groupby ([df ["padate"] .dt.month, "Rating"]) ["UserID"] .agg (pv=np.sum) df_group.head (20)

Second, use unstack to realize two-dimensional perspective of data.

Objective: to draw a picture to compare the quantitative trend of different scores by month.

Df_stack=df_group.unstack () df_stackdf_stack.plot () # unstack and stack are reciprocal operations df_stack.stack () .head (20)

Use pivot to simplify perspective

The pivot method is equivalent to creating a hierarchical index on df using set_index, and then calling unstack

Df_group.head (20) df_reset=df_group.reset_index () df_reset.head () df_pivot=df_reset.pivot ("padate", "Rating", "pv") df_pivot.head () df_pivot.plot ()

4. The grammar 1.stack of stack, unstack and pivot

Stack:DataFrame.stack (level=-1,dropna=True), which turns column into index, similar to turning horizontal books into vertical.

Level=-1 represents the innermost layer of a multi-tier index, and the corresponding layer of the multi-tier index can be specified by = = 0, 1, 2.

2.unstack

Unstack:DataFrame.unstack (level=-1,fill_value=None), which turns index into column, similar to turning vertical books into horizontal ones

3.pivot

Pivot:DataFrame.pivot (index=None,columns=None,values=None), specifying index,columns,values to achieve 2D perspective

Thank you for your reading, the above is the content of "how Pandas uses stack and pivot to achieve data perspective". After the study of this article, I believe you have a deeper understanding of how Pandas uses stack and pivot to achieve data perspective, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.