<jupyter_start><jupyter_text>Read Excel data using pandas<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

DATA_FILE = "data/kiel_arbeitslose.xlsx"

# Create a Pandas Dataframe
# (read the Excel data with Pandas)
df = pd.read_excel(DATA_FILE)

# print the first five rows
# print(df.head())

df_cleaned = df.dropna(how='all')
df_years = df_cleaned['Jahr'].astype(np.uint16)

x = df_years.values
y = df_cleaned['Arbeitslose'].values  # no value for 1993

fig, ax = plt.subplots()

plt.title("Arbeitslose in Kiel\n(1989 - 2016)", size="x-large")
plt.ylabel("Arbeitslose", size="x-large")
plt.xlabel("Jahr", size="x-large")

plt.plot(y, "r*-", markersize=6, linewidth=1, color='r')

ax.set_xticks(range(len(x)))
ax.set_xticklabels(x, rotation='vertical')

plt.show()<jupyter_output>Jahr  Arbeitslose
0  1989        13602
1  1990        12077
2  1991        11136
3  1992        11081
4  1994        13806
{'fraction_non_alphanumeric': 0.1344717182497332, 'fraction_numerical': 0.07043756670224119, 'mean_word_length': 4.390804597701149, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '205810', 'license': 'MIT', 'n_tokens_mistral': 425, 'n_tokens_neox': 372, 'n_words': 85, 'path': 'kiel_arbeitslose_pandas.ipynb', 'repo_name': 'niftycode/Kiel_Open_Data', 'size': '937'}